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Rahbé E, Glaser P, Opatowski L. Modeling the transmission of antibiotic-resistant Enterobacterales in the community: A systematic review. Epidemics 2024; 48:100783. [PMID: 38944024 DOI: 10.1016/j.epidem.2024.100783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/19/2024] [Accepted: 06/20/2024] [Indexed: 07/01/2024] Open
Abstract
BACKGROUND Antibiotic-resistant Enterobacterales (ARE) are a public health threat worldwide. Dissemination of these opportunistic pathogens has been largely studied in hospitals. Despite high prevalence of asymptomatic colonization in the community in some regions of the world, less is known about ARE acquisition and spread in this setting. As explaining the community ARE dynamics has not been straightforward, mathematical models can be key to explore underlying phenomena and further evaluate the impact of interventions to curb ARE circulation outside of hospitals. METHODS We conducted a systematic review of mathematical modeling studies focusing on the transmission of AR-E in the community, excluding models only specific to hospitals. We extracted model features (population, setting), formalism (compartmental, individual-based), biological hypotheses (transmission, infection, antibiotic impact, resistant strain specificities) and main findings. We discussed additional mechanisms to be considered, open scientific questions, and most pressing data needs. RESULTS We identified 18 modeling studies focusing on the human transmission of ARE in the community (n=11) or in both community and hospital (n=7). Models aimed at (i) understanding mechanisms driving resistance dynamics; (ii) identifying and quantifying transmission routes; or (iii) evaluating public health interventions to reduce resistance. To overcome the difficulty of reproducing observed ARE dynamics in the community using the classical two-strains competition model, studies proposed to include mechanisms such as within-host strain competition or a strong host population structure. Studies inferring model parameters from longitudinal carriage data were mostly based on models considering the ARE strain only. They showed differences in ARE carriage duration depending on the acquisition mode: returning travelers have a significantly shorter carriage duration than discharged hospitalized patient or healthy individuals. Interestingly, predictions across models regarding the success of public health interventions to reduce ARE rates depended on pathogens, settings, and antibiotic resistance mechanisms. For E. coli, reducing person-to-person transmission in the community had a stronger effect than reducing antibiotic use in the community. For Klebsiella pneumoniae, reducing antibiotic use in hospitals was more efficient than reducing community use. CONCLUSIONS This study raises the limited number of modeling studies specifically addressing the transmission of ARE in the community. It highlights the need for model development and community-based data collection especially in low- and middle-income countries to better understand acquisition routes and their relative contribution to observed ARE levels. Such modeling will be critical to correctly design and evaluate public health interventions to control ARE transmission in the community and further reduce the associated infection burden.
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Affiliation(s)
- Eve Rahbé
- Institut Pasteur, Université Paris Cité, Epidemiology and Modelling of Antimicrobials Evasion research unit, Paris, France; Université Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology research team, Montigny-Le-Bretonneux, France.
| | - Philippe Glaser
- Institut Pasteur, Ecology and Evolution of Antibiotic Resistance research unit, Université Paris Cité, Paris, France
| | - Lulla Opatowski
- Institut Pasteur, Université Paris Cité, Epidemiology and Modelling of Antimicrobials Evasion research unit, Paris, France; Université Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology research team, Montigny-Le-Bretonneux, France.
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Yang X, Scharff R. Foodborne Illnesses from Leafy Greens in the United States: Attribution, Burden, and Cost. J Food Prot 2024; 87:100275. [PMID: 38609013 DOI: 10.1016/j.jfp.2024.100275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024]
Abstract
Leafy green vegetables are a major source of foodborne illnesses. Nevertheless, few studies have attempted to estimate attribution and burden of illness estimates for leafy greens. This study combines results from three outbreak-based attribution models with illness incidence and economic cost models to develop comprehensive pathogen-specific burden estimates for leafy greens and their subcategories in the United States. We find that up to 9.18% (90% CI: 5.81%-15.18%) of foodborne illnesses linked to identified pathogens are attributed to leafy greens. Including 'Unknown' illnesses not linked to specific pathogens, leafy greens account for as many as 2,307,558 (90% CI: 1,077,815-4,075,642) illnesses annually in the United States. The economic cost of these illnesses is estimated to be up to $5.278 billion (90% CI: $3.230-$8.221 billion) annually. Excluding the pathogens with small outbreak sizes, Norovirus, Shiga toxin-producingEscherichia coli (both non-O157 and O157:H7), Campylobacter spp., and nontyphoidal Salmonella, are associated with the highest number of illnesses and greatest costs from leafy greens. While lettuce (romaine, iceberg, "other lettuce") takes 60.8% of leafy green outbreaks, it accounts for up to 75.7% of leafy green foodborne illnesses and 70% of costs. Finally, we highlighted that 19.8% of Shiga toxin-producingEscherichia coli O157:H7 illnesses are associated with romaine among all food commodities, resulting in 12,496 estimated illnesses and $324.64 million annually in the United States.
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Affiliation(s)
- Xuerui Yang
- Department of Human Science, The Ohio State University, Columbus, OH, USA.
| | - Robert Scharff
- Department of Human Science, The Ohio State University, Columbus, OH, USA
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Guzinski J, Tang Y, Chattaway MA, Dallman TJ, Petrovska L. Development and validation of a random forest algorithm for source attribution of animal and human Salmonella Typhimurium and monophasic variants of S. Typhimurium isolates in England and Wales utilising whole genome sequencing data. Front Microbiol 2024; 14:1254860. [PMID: 38533130 PMCID: PMC10963456 DOI: 10.3389/fmicb.2023.1254860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 12/22/2023] [Indexed: 03/28/2024] Open
Abstract
Source attribution has traditionally involved combining epidemiological data with different pathogen characterisation methods, including 7-gene multi locus sequence typing (MLST) or serotyping, however, these approaches have limited resolution. In contrast, whole genome sequencing data provide an overview of the whole genome that can be used by attribution algorithms. Here, we applied a random forest (RF) algorithm to predict the primary sources of human clinical Salmonella Typhimurium (S. Typhimurium) and monophasic variants (monophasic S. Typhimurium) isolates. To this end, we utilised single nucleotide polymorphism diversity in the core genome MLST alleles obtained from 1,061 laboratory-confirmed human and animal S. Typhimurium and monophasic S. Typhimurium isolates as inputs into a RF model. The algorithm was used for supervised learning to classify 399 animal S. Typhimurium and monophasic S. Typhimurium isolates into one of eight distinct primary source classes comprising common livestock and pet animal species: cattle, pigs, sheep, other mammals (pets: mostly dogs and horses), broilers, layers, turkeys, and game birds (pheasants, quail, and pigeons). When applied to the training set animal isolates, model accuracy was 0.929 and kappa 0.905, whereas for the test set animal isolates, for which the primary source class information was withheld from the model, the accuracy was 0.779 and kappa 0.700. Subsequently, the model was applied to assign 662 human clinical cases to the eight primary source classes. In the dataset, 60/399 (15.0%) of the animal and 141/662 (21.3%) of the human isolates were associated with a known outbreak of S. Typhimurium definitive type (DT) 104. All but two of the 141 DT104 outbreak linked human isolates were correctly attributed by the model to the primary source classes identified as the origin of the DT104 outbreak. A model that was run without the clonal DT104 animal isolates produced largely congruent outputs (training set accuracy 0.989 and kappa 0.985; test set accuracy 0.781 and kappa 0.663). Overall, our results show that RF offers considerable promise as a suitable methodology for epidemiological tracking and source attribution for foodborne pathogens.
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Affiliation(s)
- Jaromir Guzinski
- Animal and Plant Health Agency, Bacteriology Department, Addlestone, United Kingdom
| | - Yue Tang
- Animal and Plant Health Agency, Bacteriology Department, Addlestone, United Kingdom
| | - Marie Anne Chattaway
- Gastrointestinal Bacteria Reference Unit, UK Health Security Agency, London, United Kingdom
| | - Timothy J. Dallman
- Gastrointestinal Bacteria Reference Unit, UK Health Security Agency, London, United Kingdom
| | - Liljana Petrovska
- Animal and Plant Health Agency, Bacteriology Department, Addlestone, United Kingdom
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Cardim Falcao R, Edwards MR, Hurst M, Fraser E, Otterstatter M. A Review on Microbiological Source Attribution Methods of Human Salmonellosis: From Subtyping to Whole-Genome Sequencing. Foodborne Pathog Dis 2024; 21:137-146. [PMID: 38032610 PMCID: PMC10924193 DOI: 10.1089/fpd.2023.0075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023] Open
Abstract
Salmonella is one of the main causes of human foodborne illness. It is endemic worldwide, with different animals and animal-based food products as reservoirs and vehicles of infection. Identifying animal reservoirs and potential transmission pathways of Salmonella is essential for prevention and control. There are many approaches for source attribution, each using different statistical models and data streams. Some aim to identify the animal reservoir, while others aim to determine the point at which exposure occurred. With the advance of whole-genome sequencing (WGS) technologies, new source attribution models will greatly benefit from the discriminating power gained with WGS. This review discusses some key source attribution methods and their mathematical and statistical tools. We also highlight recent studies utilizing WGS for source attribution and discuss open questions and challenges in developing new WGS methods. We aim to provide a better understanding of the current state of these methodologies with application to Salmonella and other foodborne pathogens that are common sources of illness in the poultry and human sectors.
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Affiliation(s)
- Rebeca Cardim Falcao
- British Columbia Centre for Disease Control, Vancouver, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, Canada
| | - Megan R Edwards
- British Columbia Centre for Disease Control, Vancouver, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, Canada
| | - Matt Hurst
- Public Health Agency of Canada, Guelph, Canada
| | - Erin Fraser
- British Columbia Centre for Disease Control, Vancouver, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, Canada
| | - Michael Otterstatter
- British Columbia Centre for Disease Control, Vancouver, Canada
- School of Population and Public Health, The University of British Columbia, Vancouver, Canada
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McLure A, Smith JJ, Firestone SM, Kirk MD, French N, Fearnley E, Wallace R, Valcanis M, Bulach D, Moffatt CRM, Selvey LA, Jennison A, Cribb DM, Glass K. Source attribution of campylobacteriosis in Australia, 2017-2019. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2023; 43:2527-2548. [PMID: 37032319 PMCID: PMC10947381 DOI: 10.1111/risa.14138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 02/02/2023] [Accepted: 02/09/2023] [Indexed: 06/19/2023]
Abstract
Campylobacter jejuni and Campylobacter coli infections are the leading cause of foodborne gastroenteritis in high-income countries. Campylobacter colonizes a variety of warm-blooded hosts that are reservoirs for human campylobacteriosis. The proportions of Australian cases attributable to different animal reservoirs are unknown but can be estimated by comparing the frequency of different sequence types in cases and reservoirs. Campylobacter isolates were obtained from notified human cases and raw meat and offal from the major livestock in Australia between 2017 and 2019. Isolates were typed using multi-locus sequence genotyping. We used Bayesian source attribution models including the asymmetric island model, the modified Hald model, and their generalizations. Some models included an "unsampled" source to estimate the proportion of cases attributable to wild, feral, or domestic animal reservoirs not sampled in our study. Model fits were compared using the Watanabe-Akaike information criterion. We included 612 food and 710 human case isolates. The best fitting models attributed >80% of Campylobacter cases to chickens, with a greater proportion of C. coli (>84%) than C. jejuni (>77%). The best fitting model that included an unsampled source attributed 14% (95% credible interval [CrI]: 0.3%-32%) to the unsampled source and only 2% to ruminants (95% CrI: 0.3%-12%) and 2% to pigs (95% CrI: 0.2%-11%) The best fitting model that did not include an unsampled source attributed 12% to ruminants (95% CrI: 1.3%-33%) and 6% to pigs (95% CrI: 1.1%-19%). Chickens were the leading source of human Campylobacter infections in Australia in 2017-2019 and should remain the focus of interventions to reduce burden.
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Affiliation(s)
- Angus McLure
- National Centre for Epidemiology and Population HealthThe Australian National UniversityCanberraAustralia
| | - James J. Smith
- Food Safety Standards and Regulation, Health Protection BranchQueensland HealthBrisbaneAustralia
- School of Biology and Environmental Science, Faculty of ScienceQueensland University of TechnologyBrisbaneAustralia
| | - Simon Matthew Firestone
- Melbourne Veterinary School, Faculty of ScienceThe University of MelbourneMelbourneAustralia
| | - Martyn D. Kirk
- National Centre for Epidemiology and Population HealthThe Australian National UniversityCanberraAustralia
| | - Nigel French
- Infectious Disease Research Centre, Hopkirk Research InstituteMassey UniversityPalmerston NorthNew Zealand
- New Zealand Food Safety Science and Research Centre, Hopkirk Research InstituteMassey UniversityPalmerston NorthNew Zealand
| | - Emily Fearnley
- Department for Health and WellbeingGovernment of South AustraliaAdelaideAustralia
| | - Rhiannon Wallace
- Agassiz Research and Development Centre, Agriculture and Agri‐Food CanadaAgassizCanada
| | - Mary Valcanis
- The Doherty Institute for Infection and ImmunityMelbourneAustralia
- Microbiological Diagnostic Unit Public Health LaboratoryThe University of MelbourneMelbourneAustralia
| | - Dieter Bulach
- The Doherty Institute for Infection and ImmunityMelbourneAustralia
- Melbourne BioinformaticsThe University of MelbourneMelbourneAustralia
| | - Cameron R. M. Moffatt
- National Centre for Epidemiology and Population HealthThe Australian National UniversityCanberraAustralia
| | - Linda A. Selvey
- School of Public Health, Faculty of MedicineThe University of QueenslandBrisbaneAustralia
| | - Amy Jennison
- Public Health Microbiology, Forensic and Scientific Services, Queensland HealthBrisbaneAustralia
| | - Danielle M. Cribb
- National Centre for Epidemiology and Population HealthThe Australian National UniversityCanberraAustralia
| | - Kathryn Glass
- National Centre for Epidemiology and Population HealthThe Australian National UniversityCanberraAustralia
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Castelli P, De Ruvo A, Bucciacchio A, D'Alterio N, Cammà C, Di Pasquale A, Radomski N. Harmonization of supervised machine learning practices for efficient source attribution of Listeria monocytogenes based on genomic data. BMC Genomics 2023; 24:560. [PMID: 37736708 PMCID: PMC10515079 DOI: 10.1186/s12864-023-09667-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 09/10/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Genomic data-based machine learning tools are promising for real-time surveillance activities performing source attribution of foodborne bacteria such as Listeria monocytogenes. Given the heterogeneity of machine learning practices, our aim was to identify those influencing the source prediction performance of the usual holdout method combined with the repeated k-fold cross-validation method. METHODS A large collection of 1 100 L. monocytogenes genomes with known sources was built according to several genomic metrics to ensure authenticity and completeness of genomic profiles. Based on these genomic profiles (i.e. 7-locus alleles, core alleles, accessory genes, core SNPs and pan kmers), we developed a versatile workflow assessing prediction performance of different combinations of training dataset splitting (i.e. 50, 60, 70, 80 and 90%), data preprocessing (i.e. with or without near-zero variance removal), and learning models (i.e. BLR, ERT, RF, SGB, SVM and XGB). The performance metrics included accuracy, Cohen's kappa, F1-score, area under the curves from receiver operating characteristic curve, precision recall curve or precision recall gain curve, and execution time. RESULTS The testing average accuracies from accessory genes and pan kmers were significantly higher than accuracies from core alleles or SNPs. While the accuracies from 70 and 80% of training dataset splitting were not significantly different, those from 80% were significantly higher than the other tested proportions. The near-zero variance removal did not allow to produce results for 7-locus alleles, did not impact significantly the accuracy for core alleles, accessory genes and pan kmers, and decreased significantly accuracy for core SNPs. The SVM and XGB models did not present significant differences in accuracy between each other and reached significantly higher accuracies than BLR, SGB, ERT and RF, in this order of magnitude. However, the SVM model required more computing power than the XGB model, especially for high amount of descriptors such like core SNPs and pan kmers. CONCLUSIONS In addition to recommendations about machine learning practices for L. monocytogenes source attribution based on genomic data, the present study also provides a freely available workflow to solve other balanced or unbalanced multiclass phenotypes from binary and categorical genomic profiles of other microorganisms without source code modifications.
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Affiliation(s)
- Pierluigi Castelli
- Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise "Giuseppe Caporale" (IZSAM), National Reference Centre (NRC) for Whole Genome Sequencing of microbial pathogens: data base and bioinformatics analysis (GENPAT), Via Campo Boario, Teramo, TE, 64100, Italy
| | - Andrea De Ruvo
- Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise "Giuseppe Caporale" (IZSAM), National Reference Centre (NRC) for Whole Genome Sequencing of microbial pathogens: data base and bioinformatics analysis (GENPAT), Via Campo Boario, Teramo, TE, 64100, Italy
| | - Andrea Bucciacchio
- Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise "Giuseppe Caporale" (IZSAM), National Reference Centre (NRC) for Whole Genome Sequencing of microbial pathogens: data base and bioinformatics analysis (GENPAT), Via Campo Boario, Teramo, TE, 64100, Italy
| | - Nicola D'Alterio
- Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise "Giuseppe Caporale" (IZSAM), National Reference Centre (NRC) for Whole Genome Sequencing of microbial pathogens: data base and bioinformatics analysis (GENPAT), Via Campo Boario, Teramo, TE, 64100, Italy
| | - Cesare Cammà
- Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise "Giuseppe Caporale" (IZSAM), National Reference Centre (NRC) for Whole Genome Sequencing of microbial pathogens: data base and bioinformatics analysis (GENPAT), Via Campo Boario, Teramo, TE, 64100, Italy
| | - Adriano Di Pasquale
- Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise "Giuseppe Caporale" (IZSAM), National Reference Centre (NRC) for Whole Genome Sequencing of microbial pathogens: data base and bioinformatics analysis (GENPAT), Via Campo Boario, Teramo, TE, 64100, Italy
| | - Nicolas Radomski
- Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise "Giuseppe Caporale" (IZSAM), National Reference Centre (NRC) for Whole Genome Sequencing of microbial pathogens: data base and bioinformatics analysis (GENPAT), Via Campo Boario, Teramo, TE, 64100, Italy.
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Hamad GM, Samy H, Mehany T, Korma SA, Eskander M, Tawfik RG, EL-Rokh GEA, Mansour AM, Saleh SM, EL Sharkawy A, Abdelfttah HEA, Khalifa E. Utilization of Algae Extracts as Natural Antibacterial and Antioxidants for Controlling Foodborne Bacteria in Meat Products. Foods 2023; 12:3281. [PMID: 37685214 PMCID: PMC10486444 DOI: 10.3390/foods12173281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 08/28/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023] Open
Abstract
Padina pavonica, Hormophysa cuneiformis, and Corallina officinalis are three types of algae that are assumed to be used as antibacterial agents. Our study's goal was to look into algal extracts' potential to be used as food preservative agents and to evaluate their ability to inhibit pathogenic bacteria in several meat products (pastirma, beef burger, luncheon, minced meat, and kofta) from the local markets in Alexandria, Egypt. By testing their antibacterial activity, results demonstrated that Padina pavonica showed the highest antibacterial activity towards Bacillus cereus, Staphylococcus aureus, Escherichia coli, Streptococcus pyogenes, Salmonella spp., and Klebsiella pneumoniae. Padina pavonica extract also possesses most phenolic and flavonoid content overall. It has 24 mg gallic acid equivalent/g and 7.04 mg catechol equivalent/g, respectively. Moreover, the algae extracts were tested for their antioxidant activity, and the findings were measured using ascorbic acid as a benchmark. The IC50 of ascorbic acid was found to be 25.09 μg/mL, while Padina pavonica exhibited an IC50 value of 267.49 μg/mL, Corallina officinalis 305.01 μg/mL, and Hormophysa cuneiformis 325.23 μg/mL. In this study, Padina pavonica extract was utilized in three different concentrations (Treatment 1 g/100 g, Treatment 2 g/100 g, and Treatment 3 g/100 g) on beef burger as a model. The results showed that as the concentration of the extract increased, the bacterial inhibition increased over time. Bacillus cereus was found to be the most susceptible to the extract, while Streptococcus pyogenes was the least. In addition, Padina pavonica was confirmed to be a safe compound through cytotoxicity testing. After conducting a sensory evaluation test, it was confirmed that Padina pavonica in meat products proved to be a satisfactory product.
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Affiliation(s)
- Gamal M. Hamad
- Food Technology Department, Arid Lands Cultivation Research Institute (ALCRI), City of Scientific Research and Technological Applications (SRTA-City), New Borg El-Arab 21934, Egypt;
| | - Haneen Samy
- Biotechnology and Chemistry Department, Faculty of Science, Alexandria University, Alexandria 22758, Egypt;
| | - Taha Mehany
- Food Technology Department, Arid Lands Cultivation Research Institute (ALCRI), City of Scientific Research and Technological Applications (SRTA-City), New Borg El-Arab 21934, Egypt;
| | - Sameh A. Korma
- Department of Food Science, Faculty of Agriculture, Zagazig University, Zagazig 44519, Egypt;
| | - Michael Eskander
- Department of Food Hygiene and Control, Faculty of Veterinary Medicine, Alexandria University, Alexandria 22758, Egypt;
| | - Rasha G. Tawfik
- Department of Microbiology, Faculty of Veterinary Medicine, Alexandria University, Alexandria 22758, Egypt;
| | - Gamal E. A. EL-Rokh
- Department of Food Science and Technology, Faculty of Agriculture, Al-Azhar University, Assiut 71524, Egypt; (G.E.A.E.-R.); (H.E.A.A.)
| | - Alaa M. Mansour
- Department of Animal Hygiene and Zoonoses, Faculty of Veterinary Medicine, Alexandria University, Alexandria 22758, Egypt;
| | - Samaa M. Saleh
- Department of Food Science, Faculty of Agriculture (Saba Basha), Alexandria University, Alexandria 21531, Egypt;
| | - Amany EL Sharkawy
- National Institute of Oceanography and Fisheries (NIOF), Cairo 11516, Egypt;
| | - Hesham E. A. Abdelfttah
- Department of Food Science and Technology, Faculty of Agriculture, Al-Azhar University, Assiut 71524, Egypt; (G.E.A.E.-R.); (H.E.A.A.)
| | - Eman Khalifa
- Department of Microbiology, Faculty of Veterinary Medicine, Matrouh University, Matrouh 51511, Egypt
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Nouws S, Verhaegen B, Denayer S, Crombé F, Piérard D, Bogaerts B, Vanneste K, Marchal K, Roosens NHC, De Keersmaecker SCJ. Transforming Shiga toxin-producing Escherichia coli surveillance through whole genome sequencing in food safety practices. Front Microbiol 2023; 14:1204630. [PMID: 37520372 PMCID: PMC10381951 DOI: 10.3389/fmicb.2023.1204630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 06/22/2023] [Indexed: 08/01/2023] Open
Abstract
Introduction Shiga toxin-producing Escherichia coli (STEC) is a gastrointestinal pathogen causing foodborne outbreaks. Whole Genome Sequencing (WGS) in STEC surveillance holds promise in outbreak prevention and confinement, in broadening STEC epidemiology and in contributing to risk assessment and source attribution. However, despite international recommendations, WGS is often restricted to assist outbreak investigation and is not yet fully implemented in food safety surveillance across all European countries, in contrast to for example in the United States. Methods In this study, WGS was retrospectively applied to isolates collected within the context of Belgian food safety surveillance and combined with data from clinical isolates to evaluate its benefits. A cross-sector WGS-based collection of 754 strains from 1998 to 2020 was analyzed. Results We confirmed that WGS in food safety surveillance allows accurate detection of genomic relationships between human cases and strains isolated from food samples, including those dispersed over time and geographical locations. Identifying these links can reveal new insights into outbreaks and direct epidemiological investigations to facilitate outbreak management. Complete WGS-based isolate characterization enabled expanding epidemiological insights related to circulating serotypes, virulence genes and antimicrobial resistance across different reservoirs. Moreover, associations between virulence genes and severe disease were determined by incorporating human metadata into the data analysis. Gaps in the surveillance system were identified and suggestions for optimization related to sample centralization, harmonizing isolation methods, and expanding sampling strategies were formulated. Discussion This study contributes to developing a representative WGS-based collection of circulating STEC strains and by illustrating its benefits, it aims to incite policymakers to support WGS uptake in food safety surveillance.
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Affiliation(s)
- Stéphanie Nouws
- Transversal Activities in Applied Genomics, Sciensano, Brussels, Belgium
- IDlab, Department of Information Technology, Ghent University—IMEC, Ghent, Belgium
| | - Bavo Verhaegen
- National Reference Laboratory for Shiga Toxin-Producing Escherichia coli (NRL STEC) and for Foodborne Outbreaks (NRL FBO), Foodborne Pathogens, Sciensano, Brussels, Belgium
| | - Sarah Denayer
- National Reference Laboratory for Shiga Toxin-Producing Escherichia coli (NRL STEC) and for Foodborne Outbreaks (NRL FBO), Foodborne Pathogens, Sciensano, Brussels, Belgium
| | - Florence Crombé
- National Reference Centre for Shiga Toxin-Producing Escherichia coli (NRC STEC), Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Brussels, Belgium
| | - Denis Piérard
- National Reference Centre for Shiga Toxin-Producing Escherichia coli (NRC STEC), Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Brussels, Belgium
| | - Bert Bogaerts
- Transversal Activities in Applied Genomics, Sciensano, Brussels, Belgium
| | - Kevin Vanneste
- Transversal Activities in Applied Genomics, Sciensano, Brussels, Belgium
| | - Kathleen Marchal
- IDlab, Department of Information Technology, Ghent University—IMEC, Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
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Townsend A, den Bakker HC, Mann A, Murphy CM, Strawn LK, Dunn LL. 16S microbiome analysis of microbial communities in distribution centers handling fresh produce. Front Microbiol 2023; 14:1041936. [PMID: 37502401 PMCID: PMC10369000 DOI: 10.3389/fmicb.2023.1041936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 05/18/2023] [Indexed: 07/29/2023] Open
Abstract
Little is known about the microbial communities found in distribution centers (DCs), especially in those storing and handling food. As many foodborne bacteria are known to establish residence in food facilities, it is reasonable to assume that DCs handling foods are also susceptible to pathogen colonization. To investigate the microbial communities within DCs, 16S amplicon sequencing was completed on 317 environmental surface sponge swabs collected in DCs (n = 18) across the United States. An additional 317 swabs were collected in parallel to determine if any viable Listeria species were also present at each sampling site. There were significant differences in median diversity measures (observed, Shannon, and Chao1) across individual DCs, and top genera across all reads were Carnobacterium_A, Psychrobacter, Pseudomonas_E, Leaf454, and Staphylococcus based on taxonomic classifications using the Genome Taxonomy Database. Of the 39 16S samples containing Listeria ASVs, four of these samples had corresponding Listeria positive microbiological samples. Data indicated a predominance of ASVs identified as cold-tolerant bacteria in environmental samples collected in DCs. Differential abundance analysis identified Carnobacterium_A, Psychrobacter, and Pseudomonas_E present at a significantly greater abundance in Listeria positive microbiological compared to those negative for Listeria. Additionally, microbiome composition varied significantly across groupings within variables (e.g., DC, season, general sampling location).
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Affiliation(s)
- Anna Townsend
- Department of Food Science and Technology, University of Georgia, Athens, GA, United States
| | - Hendrik C. den Bakker
- Center for Food Safety, Department of Food Science and Technology, University of Georgia, Griffin, GA, United States
| | - Amy Mann
- Center for Food Safety, Department of Food Science and Technology, University of Georgia, Griffin, GA, United States
| | - Claire M. Murphy
- Department of Food Science and Technology, Virginia Tech, Blacksburg, VA, United States
| | - Laura K. Strawn
- Department of Food Science and Technology, Virginia Tech, Blacksburg, VA, United States
| | - Laurel L. Dunn
- Department of Food Science and Technology, University of Georgia, Athens, GA, United States
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10
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Bolzoni L, Bonardi S, Tansini C, Scaltriti E, Menozzi I, Morganti M, Conter M, Pongolini S. Different Roles of Wild Boars and Livestock in Salmonella Transmission to Humans in Italy. ECOHEALTH 2023; 20:122-132. [PMID: 36918504 PMCID: PMC10014403 DOI: 10.1007/s10393-023-01625-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 01/31/2023] [Indexed: 06/11/2023]
Abstract
Wild boar (Sus scrofa) is the most widely distributed large wildlife mammal worldwide. To investigate the transmission of Salmonella enterica amongst wild boars (Sus scrofa), humans, and livestock, we compared via pulsed-field gel electrophoresis and whole genome sequences the isolates of S. enterica serovar Typhimurium (biphasic and monophasic variants) and Enteritidis collected from wild boars, food-producing animals, and human patients in Emilia-Romagna region (Northern Italy) between 2017 and 2020. Specifically, we analysed 2175 isolates originated from human (1832), swine (117), bovine (128), poultry (76), and wild boar (22). The genomic analyses showed that wild boars shared most of their lineages of biphasic Typhimurium with bovines and most of Enteritidis with poultry, whilst we did not find any lineage shared with swine. Moreover, almost 17% of human biphasic Typhimurium and Enteritidis belonged to genomic clusters including wild boar isolates, but the inclusion of bovine and poultry isolates in the same clusters and the peculiar spatial distribution of the isolates suggested that human cases (and wild boar infections) likely originated from bovines and poultry. Consequently, wild boars appear not to play a significant role in infecting humans with these serovars, but seem to get infected themselves from livestock, probably through the environment.
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Affiliation(s)
- Luca Bolzoni
- Risk Analysis and Genomic Epidemiology Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia-Romagna, Sezione di Parma, Strada dei Mercati 13/A, 43126, Parma, Italy
| | - Silvia Bonardi
- Department of Veterinary Science, Unit of Inspection of Food of Animal Origin, University of Parma, Strada del Taglio 10, 43126, Parma, Italy.
| | - Cesare Tansini
- Department of Veterinary Science, Unit of Inspection of Food of Animal Origin, University of Parma, Strada del Taglio 10, 43126, Parma, Italy
| | - Erica Scaltriti
- Risk Analysis and Genomic Epidemiology Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia-Romagna, Sezione di Parma, Strada dei Mercati 13/A, 43126, Parma, Italy
| | - Ilaria Menozzi
- Risk Analysis and Genomic Epidemiology Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia-Romagna, Sezione di Parma, Strada dei Mercati 13/A, 43126, Parma, Italy
| | - Marina Morganti
- Risk Analysis and Genomic Epidemiology Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia-Romagna, Sezione di Parma, Strada dei Mercati 13/A, 43126, Parma, Italy
| | - Mauro Conter
- Department of Veterinary Science, Unit of Inspection of Food of Animal Origin, University of Parma, Strada del Taglio 10, 43126, Parma, Italy
| | - Stefano Pongolini
- Risk Analysis and Genomic Epidemiology Unit, Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia-Romagna, Sezione di Parma, Strada dei Mercati 13/A, 43126, Parma, Italy
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11
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Ali S, Alsayeqh AF. Review of major meat-borne zoonotic bacterial pathogens. Front Public Health 2022; 10:1045599. [PMID: 36589940 PMCID: PMC9799061 DOI: 10.3389/fpubh.2022.1045599] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 11/18/2022] [Indexed: 12/16/2022] Open
Abstract
The importance of meat-borne pathogens to global disease transmission and food safety is significant for public health. These pathogens, which can cause a variety of diseases, include bacteria, viruses, fungi, and parasites. The consumption of pathogen-contaminated meat or meat products causes a variety of diseases, including gastrointestinal ailments. Humans are susceptible to several diseases caused by zoonotic bacterial pathogens transmitted through meat consumption, most of which damage the digestive system. These illnesses are widespread worldwide, with the majority of the burden borne by developing countries. Various production, processing, transportation, and food preparation stages can expose meat and meat products to bacterial infections and/or toxins. Worldwide, bacterial meat-borne diseases are caused by strains of Escherichia coli, Salmonella, Listeria monocytogenes, Shigella, Campylobacter, Brucella, Mycobacterium bovis, and toxins produced by Staphylococcus aureus, Clostridium species, and Bacillus cereus. Additionally, consuming contaminated meat or meat products with drug-resistant bacteria is a severe public health hazard. Controlling zoonotic bacterial pathogens demands intervention at the interface between humans, animals, and their environments. This review aimed to highlight the significance of meat-borne bacterial zoonotic pathogens while adhering to the One Health approach for creating efficient control measures.
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Affiliation(s)
- Sultan Ali
- Institute of Microbiology, Faculty of Veterinary Science, University of Agriculture, Faisalabad, Pakistan
| | - Abdullah F. Alsayeqh
- Department of Veterinary Medicine, College of Agriculture and Veterinary Medicine, Qassim University, Buraidah, Saudi Arabia
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12
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Tanui CK, Benefo EO, Karanth S, Pradhan AK. A Machine Learning Model for Food Source Attribution of Listeria monocytogenes. Pathogens 2022; 11:pathogens11060691. [PMID: 35745545 PMCID: PMC9230378 DOI: 10.3390/pathogens11060691] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/06/2022] [Accepted: 06/10/2022] [Indexed: 12/07/2022] Open
Abstract
Despite its low morbidity, listeriosis has a high mortality rate due to the severity of its clinical manifestations. The source of human listeriosis is often unclear. In this study, we investigate the ability of machine learning to predict the food source from which clinical Listeria monocytogenes isolates originated. Four machine learning classification algorithms were trained on core genome multilocus sequence typing data of 1212 L. monocytogenes isolates from various food sources. The average accuracies of random forest, support vector machine radial kernel, stochastic gradient boosting, and logit boost were found to be 0.72, 0.61, 0.7, and 0.73, respectively. Logit boost showed the best performance and was used in model testing on 154 L. monocytogenes clinical isolates. The model attributed 17.5 % of human clinical cases to dairy, 32.5% to fruits, 14.3% to leafy greens, 9.7% to meat, 4.6% to poultry, and 18.8% to vegetables. The final model also provided us with genetic features that were predictive of specific sources. Thus, this combination of genomic data and machine learning-based models can greatly enhance our ability to track L. monocytogenes from different food sources.
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Affiliation(s)
- Collins K. Tanui
- Department of Nutrition and Food Science, University of Maryland, College Park, MD 20742, USA; (C.K.T.); (E.O.B.); (S.K.)
- Center for Food Safety and Security Systems, University of Maryland, College Park, MD 20742, USA
| | - Edmund O. Benefo
- Department of Nutrition and Food Science, University of Maryland, College Park, MD 20742, USA; (C.K.T.); (E.O.B.); (S.K.)
| | - Shraddha Karanth
- Department of Nutrition and Food Science, University of Maryland, College Park, MD 20742, USA; (C.K.T.); (E.O.B.); (S.K.)
| | - Abani K. Pradhan
- Department of Nutrition and Food Science, University of Maryland, College Park, MD 20742, USA; (C.K.T.); (E.O.B.); (S.K.)
- Center for Food Safety and Security Systems, University of Maryland, College Park, MD 20742, USA
- Correspondence:
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13
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McLure A, Shadbolt C, Desmarchelier PM, Kirk MD, Glass K. Source attribution of salmonellosis by time and geography in New South Wales, Australia. BMC Infect Dis 2022; 22:14. [PMID: 34983395 PMCID: PMC8725445 DOI: 10.1186/s12879-021-06950-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 11/25/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Salmonella is a major cause of zoonotic illness around the world, arising from direct or indirect contact with a range of animal reservoirs. In the Australian state of New South Wales (NSW), salmonellosis is believed to be primarily foodborne, but the relative contribution of animal reservoirs is unknown. METHODS The analysis included 4543 serotyped isolates from animal reservoirs and 30,073 serotyped isolates from domestically acquired human cases in NSW between January 2008 and August 2019. We used a Bayesian source attribution methodology to estimate the proportion of foodborne Salmonella infections attributable to broiler chickens, layer chickens, ruminants, pigs, and an unknown or unsampled source. Additional analyses included covariates for four time periods and five levels of rurality. RESULTS A single serotype, S. Typhimurium, accounted for 65-75% of included cases during 2008-2014 but < 50% during 2017-2019. Attribution to layer chickens was highest during 2008-2010 (48.7%, 95% CrI 24.2-70.3%) but halved by 2017-2019 (23.1%, 95% CrI 5.7-38.9%) and was lower in the rural and remote populations than in the majority urban population. The proportion of cases attributed to the unsampled source was 11.3% (95% CrI 1.2%-22.1%) overall, but higher in rural and remote populations. The proportion of cases attributed to pork increased from approximately 20% in 2009-2016 to approximately 40% in 2017-2019, coinciding with a rise in cases due to Salmonella ser. 4,5,12:i:-. CONCLUSION Layer chickens were likely the primary reservoir of domestically acquired Salmonella infections in NSW circa 2010, but attribution to the source declined contemporaneously with increased vaccination of layer flocks and tighter food safety regulations for the handling of eggs.
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Affiliation(s)
- Angus McLure
- National Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia.
| | - Craig Shadbolt
- New South Wales Department of Primary Industries, New South Wales, Australia
| | | | - Martyn D Kirk
- National Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia
| | - Kathryn Glass
- National Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia
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14
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Pasquali F, Remondini D, Snary EL, Hald T, Guillier L. Editorial: Integrating Whole Genome Sequencing Into Source Attribution and Risk Assessment of Foodborne Bacterial Pathogens. Front Microbiol 2021; 12:795098. [PMID: 34899675 PMCID: PMC8661528 DOI: 10.3389/fmicb.2021.795098] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 11/01/2021] [Indexed: 11/20/2022] Open
Affiliation(s)
- Frederique Pasquali
- Department of Agricultural and Food Sciences, Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Daniel Remondini
- Department of Physics and Astronomy, Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Emma Louise Snary
- Department of Epidemiological Sciences, Animal and Plant Health Agency (APHA), Addlestone, United Kingdom
| | - Tine Hald
- National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Laurent Guillier
- Department of Risk Assessment, Agence Nationale de Sécurité Sanitaire de l'Alimentation, de l'Environnement et du Travail (ANSES), Maisons-Alfort, France
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15
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Deng H, Exel KE, Swart A, Bonačić Marinović AA, Dam-Deisz C, van der Giessen JWB, Opsteegh M. Digging into Toxoplasma gondii infections via soil: A quantitative microbial risk assessment approach. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 755:143232. [PMID: 33160663 DOI: 10.1016/j.scitotenv.2020.143232] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 10/05/2020] [Accepted: 10/16/2020] [Indexed: 06/11/2023]
Abstract
Soil has been identified as an important source of exposure to a variety of chemical and biological contaminants. Toxoplasma gondii is one of those potential biological contaminants associated with serious health effects in pregnant women and immunocompromised patients. Gardening or consumption of homegrown vegetables may present an important route of T. gondii infection via accidental ingestion of soil. In the Netherlands, there is quantitative information on the risk of T. gondii infection via meat products, but not on the risk of infection through soil. The objective of this study was to develop a quantitative microbial risk assessment (QMRA) model for estimating the risk associated with T. gondii exposure via accidental soil ingestion in the Netherlands. In order to obtain the needed information, a magnetic capture method for detection of T. gondii oocysts in soil samples was developed, and T. gondii DNA was detected using qPCR targeting the 529 bp repeat element. The method was shown to provide 95% probability of detection (95% CI: 88-100%) when at least 34 oocysts are present in 25 g of soil. T. gondii DNA was detected in 5 of 148 soil samples with interpretable results (3%, 95% CI: 1.5-7.7%). Results for 18 samples were not interpretable due to PCR inhibition. The estimated amount of oocysts presented in qPCR positive samples was quantified by a linear model, and the amount varied from 8 to 478 in 25 g of soil. The estimated incidence rate of T. gondii infection from the QMRA model via soil varied from 0.3 to 1.8 per 1000 individuals per day. Several data gaps (e.g., soil contamination/ingestion and oocysts viability) have been identified in this study, the structure of the model can be applied to obtain more accurate estimates of the risk of T. gondii infection via soil when data become available.
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Affiliation(s)
- Huifang Deng
- National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven, the Netherlands.
| | - Kitty E Exel
- National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven, the Netherlands; Faculty of Veterinary Medicine, Utrecht University, Yalelaan 7, 3584 CL Utrecht, the Netherlands.
| | - Arno Swart
- National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven, the Netherlands.
| | - Axel A Bonačić Marinović
- National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven, the Netherlands.
| | - Cecile Dam-Deisz
- National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven, the Netherlands.
| | - Johanna W B van der Giessen
- National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven, the Netherlands.
| | - Marieke Opsteegh
- National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven, the Netherlands.
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16
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Ben Romdhane R, Merle R. The Data Behind Risk Analysis of Campylobacter Jejuni and Campylobacter Coli Infections. Curr Top Microbiol Immunol 2021; 431:25-58. [PMID: 33620647 DOI: 10.1007/978-3-030-65481-8_2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Campylobacter jejuni and Campylobacter coli are major causes of food-borne enteritis in humans. Poultry meat is known to be responsible for a large proportion of cases of human campylobacteriosis. However, other food-borne, environmental and animal sources are frequently associated with the disease in humans as well. Human campylobacteriosis causes gastroenteritis that in most cases is self-limiting. Nevertheless, the burden of the disease is relatively large compared with other food-borne diseases, which is mostly due to rare but long-lasting symptoms related to immunological sequelae. In order to pave the way to improved surveillance and control of human campylobacteriosis, we review here the data that is typically used for risk analysis to quantify the risk and disease burden, identify specific surveillance strategies and assist in choosing the most effective control strategies. Such data are mostly collected from the literature, and their nature is discussed here, for each of the three processes that are essential for a complete risk analysis procedure: risk assessment, risk management and risk communication. Of these, the first, risk assessment, is most dependent on data, and this process is subdivided into the steps of hazard identification, hazard characterization, exposure assessment and risk characterization. For each of these steps of risk assessment, information from published material that is typically collected will be summarized here. In addition, surveillance data are highly valuable for risk assessments. Different surveillance systems are employed in different countries, which can make international comparison of data challenging. Risk analysis typically results in targeted control strategies, and these again differ between countries. The applied control strategies are as yet not sufficient to eradicate human campylobacteriosis. The surveillance tools of Campylobacter in humans and exposure sources in place in different countries are briefly reviewed to better understand the Campylobacter dynamics and guide control strategies. Finally, the available control measures on different risk factors and exposure sources are presented.
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Affiliation(s)
- Racem Ben Romdhane
- Faculty of Veterinary Medicine, Institute for Veterinary Epidemiology and Biostatistics, Freie Universität Berlin, Berlin, Germany
| | - Roswitha Merle
- Faculty of Veterinary Medicine, Institute for Veterinary Epidemiology and Biostatistics, Freie Universität Berlin, Berlin, Germany.
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17
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Mining whole genome sequence data to efficiently attribute individuals to source populations. Sci Rep 2020; 10:12124. [PMID: 32699222 PMCID: PMC7376179 DOI: 10.1038/s41598-020-68740-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 06/15/2020] [Indexed: 11/27/2022] Open
Abstract
Whole genome sequence (WGS) data could transform our ability to attribute individuals to source populations. However, methods that efficiently mine these data are yet to be developed. We present a minimal multilocus distance (MMD) method which rapidly deals with these large data sets as well as methods for optimally selecting loci. This was applied on WGS data to determine the source of human campylobacteriosis, the geographical origin of diverse biological species including humans and proteomic data to classify breast cancer tumours. The MMD method provides a highly accurate attribution which is computationally efficient for extended genotypes. These methods are generic, easy to implement for WGS and proteomic data and have wide application.
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18
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Sévellec Y, Granier SA, Le Hello S, Weill FX, Guillier L, Mistou MY, Cadel-Six S. Source Attribution Study of Sporadic Salmonella Derby Cases in France. Front Microbiol 2020; 11:889. [PMID: 32477304 PMCID: PMC7240076 DOI: 10.3389/fmicb.2020.00889] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 04/16/2020] [Indexed: 12/20/2022] Open
Abstract
Salmonella enterica subsp. enterica serovar Derby is one of the most frequent causes of gastroenteritis in humans. In Europe, this pathogen is one of the top five most commonly reported serovars in human cases. In France, S. Derby has been among the ten most frequently isolated serovars in humans since the year 2000. The main animal hosts of this serovar are pigs and poultry, and white meat is the main source of human contamination. We have previously shown that this serovar is polyphyletic and that three distinct genetic lineages of S. Derby cohabit in France. Two of them are associated with pork and one with poultry. In this study, we conducted a source attribution study based on single nucleotide polymorphism analysis of a large collection of 440 S. Derby human and non-human isolates collected in 2014-2015, to determine the contribution of each lineage to human contamination. In France, the two lineages associated with pork strains, and corresponding to the multilocus sequence typing (MLST) profiles ST39-ST40 and ST682 were responsible for 94% of human contaminations. Interestingly, the ST40 profile is responsible for the majority of human cases (71%). An analysis of epidemiologic data and the structure of the pork sector in France allowed us to explain the spread and the sporadic pattern of human cases that occurred in the studied period.
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Affiliation(s)
- Yann Sévellec
- Laboratoire de Sécurité des Aliments, Agence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du Travail, Université PARIS-EST, Maisons-Alfort, France
| | - Sophie A. Granier
- Laboratoire de Sécurité des Aliments, Agence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du Travail, Université PARIS-EST, Maisons-Alfort, France
- Laboratoire de Fougères, Agence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du Travail, Fougères, France
| | - Simon Le Hello
- Unité des Bactéries Pathogènes Entériques, Centre National de Référence des Escherichia coli, Shigella et Salmonella, Institut Pasteur, Paris, France
| | - François-Xavier Weill
- Unité des Bactéries Pathogènes Entériques, Centre National de Référence des Escherichia coli, Shigella et Salmonella, Institut Pasteur, Paris, France
| | - Laurent Guillier
- Laboratoire de Sécurité des Aliments, Agence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du Travail, Université PARIS-EST, Maisons-Alfort, France
| | - Michel-Yves Mistou
- Laboratoire de Sécurité des Aliments, Agence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du Travail, Université PARIS-EST, Maisons-Alfort, France
| | - Sabrina Cadel-Six
- Laboratoire de Sécurité des Aliments, Agence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du Travail, Université PARIS-EST, Maisons-Alfort, France
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19
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Filipello V, Mughini-Gras L, Gallina S, Vitale N, Mannelli A, Pontello M, Decastelli L, Allard MW, Brown EW, Lomonaco S. Attribution of Listeria monocytogenes human infections to food and animal sources in Northern Italy. Food Microbiol 2020; 89:103433. [PMID: 32138991 DOI: 10.1016/j.fm.2020.103433] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 12/16/2019] [Accepted: 01/15/2020] [Indexed: 12/16/2022]
Abstract
Listeriosis is a foodborne illness characterized by a relatively low morbidity, but a large disease burden due to the severity of clinical manifestations and the high case fatality rate. Increased listeriosis notifications have been observed in Europe since the 2000s. However, the reasons for this increase are largely unknown, with the sources of sporadic human listerioris often remaining elusive. Here we inferred the relative contributions of several putative sources of Listeria monocytogenes strains from listerioris patients in Northern Italy (Piedmont and Lombardy regions), using two established source attribution models (i.e. 'Dutch' and 'STRUCTURE') in comparative fashion. We compared the Multi-Locus Sequence Typing and Multi-Virulence-Locus Sequence Typing profiles of strains collected from beef, dairy, fish, game, mixed foods, mixed meat, pork, and poultry. Overall, 634 L. monocytogenes isolates were collected from 2005 to 2016. In total, 40 clonal complexes and 51 virulence types were identified, with 36% of the isolates belonging to possible epidemic clones (i.e. genetically related strains from unrelated outbreaks). Source attribution analysis showed that 50% of human listerioris cases (95% Confidence Interval 44-55%) could be attributed to dairy products, followed by poultry and pork (15% each), and mixed foods (15%). Since the contamination of dairy, poultry and pork products are closely linked to primary production, expanding actions currently limited to ready-to-eat products to the reservoir level may help reducing the risk of cross-contamination at the consumer level.
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Affiliation(s)
- Virginia Filipello
- University of Turin. Largo P, Braccini, 2, 10095, Grugliasco, Italy; Isituto Zooprofilattico Sperimentale Della Lombardia e Dell'Emilia Romagna, Via A. Bianchi, 9, 25124, Brescia, Italy.
| | - Lapo Mughini-Gras
- National Institute for Public Health and the Environment (RIVM), Center for Infectious Disease Control, Antonie van Leeuwenhoeklaan, 9, 3721 MA, Bilthoven, Netherlands; Utrecht University, Institute for Risk Assessment Sciences (IRAS), Yalelaan 2, 3584, CM, Utrecht, the Netherlands.
| | - Silvia Gallina
- Istituto Zooprofilattico Sperimentale Del Piemonte, Liguria e Valle D'Aosta, Via Bologna, 148, 10154, Torino, Italy.
| | - Nicoletta Vitale
- Istituto Zooprofilattico Sperimentale Del Piemonte, Liguria e Valle D'Aosta, Via Bologna, 148, 10154, Torino, Italy.
| | | | | | - Lucia Decastelli
- Istituto Zooprofilattico Sperimentale Del Piemonte, Liguria e Valle D'Aosta, Via Bologna, 148, 10154, Torino, Italy.
| | - Marc W Allard
- US Food & Drug Administration. 5001 Campus Drive, 20740, College Park, MD, USA.
| | - Eric W Brown
- US Food & Drug Administration. 5001 Campus Drive, 20740, College Park, MD, USA.
| | - Sara Lomonaco
- University of Turin. Largo P, Braccini, 2, 10095, Grugliasco, Italy; US Food & Drug Administration. 5001 Campus Drive, 20740, College Park, MD, USA.
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20
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Lim SC, Riley TV, Knight DR. One Health: the global challenge of Clostridium difficile infection. MICROBIOLOGY AUSTRALIA 2020. [DOI: 10.1071/ma20007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
The One Health concept recognises that the health of humans is interconnected to the health of animals and the environment. It encourages multidisciplinary communication and collaboration with the aim of enhancing surveillance and research and developing integrative policy frameworks. Clostridium difficile (also known as Clostridioides difficile) infection (CDI) has long been viewed as a hospital-associated (HA) enteric disease mainly linked to the use of broad-spectrum antimicrobials that cause dysbiosis in the gut and loss of ‘colonisation resistance'. However, since the early 2000s, the rate of community-associated CDI (CA-CDI) has increased to ~15% in Europe, ~30% in Australia and ~40% in the USA in populations often without obvious risk factors. Since the 1990s, it has become apparent that food animals are now a major reservoir and amplification host for C.difficile, including lineages of clinical importance. Cephalosporin antimicrobials, to which C. difficile is intrinsically resistant, were licensed for animal use in North America in 1990. By the second decade of the 21st century, there were reports of C. difficile contamination of food and the environment in general. Using whole-genome sequencing (WGS) and high-resolution typing, C. difficile isolates from humans, animals, food and the environment were proven to be genetically closely related and, in some cases, indistinguishable. This suggests possible zoonoses and/or anthroponoses, with contaminated food and the environment acting as the conduit for transmission between animals and humans. This paper summarises the key evidence that demonstrates the One Health importance of C. difficile.
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21
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Koutsoumanis K, Allende A, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Chemaly M, Davies R, De Cesare A, Hilbert F, Lindqvist R, Nauta M, Peixe L, Ru G, Simmons M, Skandamis P, Suffredini E, Jenkins C, Malorny B, Ribeiro Duarte AS, Torpdahl M, da Silva Felício MT, Guerra B, Rossi M, Herman L. Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food-borne microorganisms. EFSA J 2019; 17:e05898. [PMID: 32626197 PMCID: PMC7008917 DOI: 10.2903/j.efsa.2019.5898] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
This Opinion considers the application of whole genome sequencing (WGS) and metagenomics for outbreak investigation, source attribution and risk assessment of food‐borne pathogens. WGS offers the highest level of bacterial strain discrimination for food‐borne outbreak investigation and source‐attribution as well as potential for more precise hazard identification, thereby facilitating more targeted risk assessment and risk management. WGS improves linking of sporadic cases associated with different food products and geographical regions to a point source outbreak and can facilitate epidemiological investigations, allowing also the use of previously sequenced genomes. Source attribution may be favoured by improved identification of transmission pathways, through the integration of spatial‐temporal factors and the detection of multidirectional transmission and pathogen–host interactions. Metagenomics has potential, especially in relation to the detection and characterisation of non‐culturable, difficult‐to‐culture or slow‐growing microorganisms, for tracking of hazard‐related genetic determinants and the dynamic evaluation of the composition and functionality of complex microbial communities. A SWOT analysis is provided on the use of WGS and metagenomics for Salmonella and Shigatoxin‐producing Escherichia coli (STEC) serotyping and the identification of antimicrobial resistance determinants in bacteria. Close agreement between phenotypic and WGS‐based genotyping data has been observed. WGS provides additional information on the nature and localisation of antimicrobial resistance determinants and on their dissemination potential by horizontal gene transfer, as well as on genes relating to virulence and biological fitness. Interoperable data will play a major role in the future use of WGS and metagenomic data. Capacity building based on harmonised, quality controlled operational systems within European laboratories and worldwide is essential for the investigation of cross‐border outbreaks and for the development of international standardised risk assessments of food‐borne microorganisms.
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Mikkelä A, Ranta J, Tuominen P. A Modular Bayesian Salmonella Source Attribution Model for Sparse Data. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2019; 39:1796-1811. [PMID: 30893499 PMCID: PMC6849795 DOI: 10.1111/risa.13310] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 02/13/2019] [Accepted: 02/14/2019] [Indexed: 06/09/2023]
Abstract
Several statistical models for salmonella source attribution have been presented in the literature. However, these models have often been found to be sensitive to the model parameterization, as well as the specifics of the data set used. The Bayesian salmonella source attribution model presented here was developed to be generally applicable with small and sparse annual data sets obtained over several years. The full Bayesian model was modularized into three parts (an exposure model, a subtype distribution model, and an epidemiological model) in order to separately estimate unknown parameters in each module. The proposed model takes advantage of the consumption and overall salmonella prevalence of the studied sources, as well as bacteria typing results from adjacent years. The latter were used for a smoothed estimation of the annual relative proportions of different salmonella subtypes in each of the sources. The source-specific effects and the salmonella subtype-specific effects were included in the epidemiological model to describe the differences between sources and between subtypes in their ability to infect humans. The estimation of these parameters was based on data from multiple years. Finally, the model combines the total evidence from different modules to proportion human salmonellosis cases according to their sources. The model was applied to allocate reported human salmonellosis cases from the years 2008 to 2015 to eight food sources.
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Affiliation(s)
- Antti Mikkelä
- Risk Assessment UnitFinnish Food AuthorityHelsinkiFinland
| | - Jukka Ranta
- Risk Assessment UnitFinnish Food AuthorityHelsinkiFinland
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Liao SJ, Marshall J, Hazelton ML, French NP. Extending statistical models for source attribution of zoonotic diseases: a study of campylobacteriosis. J R Soc Interface 2019; 16:20180534. [PMID: 30958154 PMCID: PMC6364659 DOI: 10.1098/rsif.2018.0534] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2018] [Accepted: 01/09/2019] [Indexed: 11/12/2022] Open
Abstract
Preventing and controlling zoonoses through the design and implementation of public health policies requires a thorough understanding of transmission pathways. Modelling jointly the epidemiological data and genetic information of microbial isolates derived from cases provides a methodology for tracing back the source of infection. In this paper, the attribution probability for human cases of campylobacteriosis for each source, conditional on the extent to which each case resides in a rural compared to urban environment, is estimated. A model that incorporates genetic data and evolutionary processes is applied alongside a newly developed genetic-free model. We show that inference from each model is comparable except for rare microbial genotypes. Further, the effect of 'rurality' may be modelled linearly on the logit scale, with increasing rurality leading to the increasing likelihood of ruminant-sourced campylobacteriosis.
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Affiliation(s)
- Sih-Jing Liao
- School of Fundamental Sciences, Massey University, Palmerston North 4442, New Zealand
| | - Jonathan Marshall
- School of Fundamental Sciences, Massey University, Palmerston North 4442, New Zealand
| | - Martin L. Hazelton
- School of Fundamental Sciences, Massey University, Palmerston North 4442, New Zealand
| | - Nigel P. French
- mEpiLab, Infectious Disease Research Centre, School of Veterinary Science, Massey University, Palmerston North 4442, New Zealand
- New Zealand Food Safety Science & Research Centre, Massey University, Palmerston North 4442, New Zealand
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Kooh P, Ververis E, Tesson V, Boué G, Federighi M. Entomophagy and Public Health: A Review of Microbiological Hazards. Health (London) 2019. [DOI: 10.4236/health.2019.1110098] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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