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Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of Listeria monocytogenes

The application of high-throughput DNA sequencing technologies (WGS) data remain an increasingly discussed but vastly unexplored resource in the public health domain of quantitative microbial risk assessment (QMRA). This is due to challenges including high dimensionality of WGS data and heterogeneit...

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Autores principales: Njage, Patrick Murigu Kamau, Leekitcharoenphon, Pimlapas, Hansen, Lisbeth Truelstrup, Hendriksen, Rene S., Faes, Christel, Aerts, Marc, Hald, Tine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698238/
https://www.ncbi.nlm.nih.gov/pubmed/33187247
http://dx.doi.org/10.3390/microorganisms8111772
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author Njage, Patrick Murigu Kamau
Leekitcharoenphon, Pimlapas
Hansen, Lisbeth Truelstrup
Hendriksen, Rene S.
Faes, Christel
Aerts, Marc
Hald, Tine
author_facet Njage, Patrick Murigu Kamau
Leekitcharoenphon, Pimlapas
Hansen, Lisbeth Truelstrup
Hendriksen, Rene S.
Faes, Christel
Aerts, Marc
Hald, Tine
author_sort Njage, Patrick Murigu Kamau
collection PubMed
description The application of high-throughput DNA sequencing technologies (WGS) data remain an increasingly discussed but vastly unexplored resource in the public health domain of quantitative microbial risk assessment (QMRA). This is due to challenges including high dimensionality of WGS data and heterogeneity of microbial growth phenotype data. This study provides an innovative approach for modeling the impact of population heterogeneity in microbial phenotypic stress response and integrates this into predictive models inputting a high-dimensional WGS data for increased precision exposure assessment using an example of Listeria monocytogenes. Finite mixture models were used to distinguish the number of sub-populations for each of the stress phenotypes, acid, cold, salt and desiccation. Machine learning predictive models were selected from six algorithms by inputting WGS data to predict the sub-population membership of new strains with unknown stress response data. An example QMRA was conducted for cultured milk products using the strains of unknown stress phenotype to illustrate the significance of the findings of this study. Increased resistance to stress conditions leads to increased growth, the likelihood of higher exposure and probability of illness. Neglecting within-species genetic and phenotypic heterogeneity in microbial stress response may over or underestimate microbial exposure and eventual risk during QMRA.
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spelling pubmed-76982382020-11-29 Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of Listeria monocytogenes Njage, Patrick Murigu Kamau Leekitcharoenphon, Pimlapas Hansen, Lisbeth Truelstrup Hendriksen, Rene S. Faes, Christel Aerts, Marc Hald, Tine Microorganisms Article The application of high-throughput DNA sequencing technologies (WGS) data remain an increasingly discussed but vastly unexplored resource in the public health domain of quantitative microbial risk assessment (QMRA). This is due to challenges including high dimensionality of WGS data and heterogeneity of microbial growth phenotype data. This study provides an innovative approach for modeling the impact of population heterogeneity in microbial phenotypic stress response and integrates this into predictive models inputting a high-dimensional WGS data for increased precision exposure assessment using an example of Listeria monocytogenes. Finite mixture models were used to distinguish the number of sub-populations for each of the stress phenotypes, acid, cold, salt and desiccation. Machine learning predictive models were selected from six algorithms by inputting WGS data to predict the sub-population membership of new strains with unknown stress response data. An example QMRA was conducted for cultured milk products using the strains of unknown stress phenotype to illustrate the significance of the findings of this study. Increased resistance to stress conditions leads to increased growth, the likelihood of higher exposure and probability of illness. Neglecting within-species genetic and phenotypic heterogeneity in microbial stress response may over or underestimate microbial exposure and eventual risk during QMRA. MDPI 2020-11-11 /pmc/articles/PMC7698238/ /pubmed/33187247 http://dx.doi.org/10.3390/microorganisms8111772 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Njage, Patrick Murigu Kamau
Leekitcharoenphon, Pimlapas
Hansen, Lisbeth Truelstrup
Hendriksen, Rene S.
Faes, Christel
Aerts, Marc
Hald, Tine
Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of Listeria monocytogenes
title Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of Listeria monocytogenes
title_full Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of Listeria monocytogenes
title_fullStr Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of Listeria monocytogenes
title_full_unstemmed Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of Listeria monocytogenes
title_short Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of Listeria monocytogenes
title_sort quantitative microbial risk assessment based on whole genome sequencing data: case of listeria monocytogenes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698238/
https://www.ncbi.nlm.nih.gov/pubmed/33187247
http://dx.doi.org/10.3390/microorganisms8111772
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