Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Njage PMK

PROVIDER: S-EPMC7698238 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of <i>Listeria monocytogenes</i>.

Njage Patrick Murigu Kamau PMK Leekitcharoenphon Pimlapas P Hansen Lisbeth Truelstrup LT Hendriksen Rene S RS Faes Christel C Aerts Marc M Hald Tine T

Microorganisms 20201111 11

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 pr ...[more]

PMID: 33187247

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Project description:Listeria monocytogenes (L. monocytogenes) is a ubiquitous organism that can easily enter the food chain. Infection with L. monocytogenes can cause invasive listeriosis. Since 2014, in Austria, L. monocytogenes isolates from human and food/food-associated samples have been provided on a mandatory basis by food producers and laboratories to the National Reference Laboratory. Since 2017, isolates undergo routinely whole genome sequencing (WGS) and core genome Multilocus Sequence Typing (cgMLST) for cluster analyses. Aims of this study were to characterize isolates and clusters of 2017 by using WGS data and to assess the usefulness of this isolate-based surveillance for generating hypotheses on sources of invasive listeriosis in real-time. WGS data from 31 human and 1744 non-human isolates originating from 2017, were eligible for the study. A cgMLST-cluster was defined as two or more isolates differing by ?10 alleles. We extracted the sequence types (STs) from the WGS data and analyzed the food subcategories meat, fish, vegetable and diary for associations with the ten most prevalent STs among food, through calculating prevalence ratios (PR) with 95% confidence intervals (CI). The three most frequent STs among the human isolates were ST1 (7/31; 22.6%), ST155 (4/31; 12.9%) and ST451 (3/31; 9.7%) and among the non-human isolates ST451 (614/1744; 35.2%), ST8 (173/1744, 10.0%) and ST9 (117/1744; 6.7%). We found ST21 associated with vegetables (PR: 11.39, 95% CI: 8.32-15.59), ST121 and ST155 with fish (PR: 7.05, 95% CI: 4.88-10.17, PR: 3.29, 95% CI: 1.86-5.82), and ST511, ST7 and ST451 with dairy products (PR: 8.55, 95% CI: 6.65-10.99; PR: 5.05, 95% CI: 3.83-6.66, PR: 3.03, 95% CI: 2.02-4.55). We identified 132 cgMLST-clusters. Six clusters contained human isolates (ST155, ST1, ST101, ST177, ST37 and ST7) and for five of those cgMLST-based cluster analyses solely was able to hypothesize the source: an Austrian meat processing company, two Austrian cheese manufacturers and two vegetable processing companies, one based in Austria and the other in Belgium. Determining routinely STs in food isolates by WGS allows to associate STs with food products. Real-time WGS of L. monocytogenes isolates provided mandatorily, proved to be useful in promptly generating hypotheses on sources of invasive listeriosis.

Project description:Listeria monocytogenes is a foodborne pathogen that can cause invasive disease with high mortality in immunocompromised individuals and can survive in a variety of food-associated environments for a long time. L. monocytogenes clonal complex (CC) 87 is composed of ST87 and three other STs and has been identified as the most common subgroup associated with both foods and human clinical infections in China. Therefore, the persistence of CC87 L. monocytogenes in food-associated environments poses a significant concern for food safety. In this study, 83 draft genomes of CC87 L. monocytogenes, including 60 newly sequenced genomes, were analyzed with all isolates from our previous surveillance in Zigong, Sichuang, China. Sixty-eight of the studied isolates were isolated from one retail market (M1 market), while the others were from seven other markets (M2-M8 markets) in the same city. Whole-genome multilocus sequence typing (wg-MLST) and the whole-genome single nucleotide polymorphism (wg-SNP) analysis were performed. Three persistent contamination routes were identified in the M1 market, caused by 2 clusters (A and B) and a wgST31 type. Cluster A isolates were associated with the persistent contamination in a raw meat stall (M1-S77), while Cluster B isolates caused a persistent contamination in aquatic foods stalls. Five wgST31 isolates caused persistent contamination in a single aquatic stall (M1-S65). A pLM1686-like plasmid was found in all Cluster A isolates. A novel plasmid, pLM1692, a truncated pLM1686 plasmid without the cadmium, and other heavy metal resistance genes were conserved in all wgST31 isolates. By comparing persistent and putative non-persistent isolates, four genes that were all located in the prophage comK might be associated with persistence. These findings enhanced our understanding of the underlying mechanisms of contamination and assist in formulating targeted strategies for the prevention and control of L. monocytogenes transmission from the food processing chain to humans. IMPORTANCE Contamination of food by Listeria monocytogenes at retail level leads to potential consumption of contaminated food with high risk of human infection. Our previous study found persistent contamination of CC87 L. monocytogenes from a retail market in China through pulsed-field gel electrophoresis and multilocus sequence typing. In this study, whole-genome sequencing was used to obtain the highest resolution inference of the source and reasons for persistent contamination; meat grinders and minced meat were the major reservoir of persistent contamination in meat stalls, whereas fishponds were the major reservoir in seafood stalls, with different L. monocytogenes isolates involved. These isolates carried different properties such as plasmids and prophages, which may have contributed to their ability to survive or adapt to the different environments. Our findings suggest that whole-genome sequencing will be an effective surveillance tool to detect persistent L. monocytogenes contamination in retail food markets and to design new control strategies to improve food safety.

Dataset Information

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

Publications

Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of <i>Listeria monocytogenes</i>.

Similar Datasets

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