Scientific Opinion on the evaluation of molecular typing methods for major food‐borne microbiological hazards and their use for attribution modelling, outbreak investigation and scanning surveillance: Part 1 (evaluation of methods and applications)

Genotyping of Campylobacter jejuni and prediction tools of its antimicrobial resistance

Although Campylobacter jejuni is the pathogen responsible for the most common foodborne illness, tracing of the infection source remains challenging due to its highly variable genome. Therefore, one of the aim of the study was to compare three genotyping methods (MLST, PFGE, and mP-BIT) to determine the most effective genotyping tool. C. jejuni strains were divided into 4 clusters based on strain similarity in the cgMLST dendrogram. Subsequently, the dendrograms of the 3 tested methods were compared to determine the accuracy of each method compared to the reference cgMLST method. Moreover, a cost-benefit analysis has showed that MLST had the highest inverse discrimination index (97%) and required less workflow, time, fewer consumables, and low bacterial sample quantity. PFGE was shown to be obsolete both because of its low discriminatory power and the complexity of the procedure. Similarly, mP‑BIT showed low separation results, which was compensated by its high availability. Therefore, our data showed that MLST is the optimal tool for genotyping C. jejuni. Another aim was to compare the antimicrobial resistance to ciprofloxacin, erythromycin, and tetracycline in C. jejuni strains isolated from human, water, air, food, and animal samples by two gene sequence-based prediction methods and to compare them with the actual susceptibility of C. jejuni strains using the disc diffusion method. Both tools, ResFinder and RGI, synchronously predict the antimicrobial susceptibility of C. jejuni and either can be used.

Contamination of street food with multidrug-resistant Salmonella, in Ouagadougou, Burkina Faso

Background

Gastrointestinal infections are a global public health problem. In Burkina Faso, West Africa, exposure to Salmonella through the consumption of unhygienic street food represents a major risk of infection requiring detailed evaluation.

Methods

Between June 2017 and July 2018, we sampled 201 street food stalls, in 11 geographic sectors of Ouagadougou, Burkina Faso. We checked for Salmonella contamination in 201 sandwiches (one per seller), according to the ISO 6579:2002 standard. All Salmonella isolates were characterized by serotyping and antimicrobial susceptibility testing, and whole-genome sequencing was performed on a subset of isolates, to investigate their phylogenetic relationships and antimicrobial resistance determinants.

Results

The prevalence of Salmonella enterica was 17.9% (36/201) and the Salmonella isolates belonged to 16 different serotypes, the most frequent being Kentucky, Derby and Tennessee, with five isolates each. Six Salmonella isolates from serotypes Brancaster and Kentucky were multidrug-resistant (MDR). Whole-genome sequencing revealed that four of these MDR isolates belonged to the emergent S. enterica serotype Kentucky clone ST198-X1 and to an invasive lineage of S. enterica serotype Enteritidis (West African clade).

Conclusion

This study reveals a high prevalence of Salmonella spp. in sandwiches sold in Ouagadougou. The presence of MDR Salmonella in food on sale detected in this study is also matter of concern.

General scientific guidance for stakeholders on health claim applications (Revision 1)

[Table: see text] The general guidance for stakeholders on the evaluation of Article 13(1), 13(5) and 14 health claims was first published in March 2011. Since then, the Panel on Dietetic Products Nutrition and Allergies (NDA) has completed the scientific assessment of Article 13(1) claims except for claims put on hold by the European Commission, and has assessedadditional health claim applications submitted pursuant to Articles 13(5), 14 and also 19. In addition, comments received from stakeholders indicate that general issues that are common to all health claims need to be further clarified and addressed. This guidance document aims to explain the general scientific principles applied by the NDA Panel for the scientific assessmentof all health claims and outlines a series of steps for the compilation of applications. The general guidance document represents the views of the NDA Panel based on the experience gained to date with the scientific assessment of health claims, and it may be further updated, as appropriate, when additional issues are addressed.The document also aims to inform applicants of newprovisionsin the pre-submission phase and in the application procedure set out in the General Food Law, as amended by the Transparency Regulation. These new provisions are applicable to all applications submitted as of 27 March 2021. The version of this guidance published in 2016 remains applicable for applications submitted before 27 March 2021.

A Practical Method to Implement Strain-Level Metagenomics-Based Foodborne Outbreak Investigation and Source Tracking in Routine

The management of a foodborne outbreak depends on the rapid and accurate identification of the responsible food source. Conventional methods based on isolation of the pathogen from the food matrix and target-specific real-time polymerase chain reactions (qPCRs) are used in routine. In recent years, the use of whole genome sequencing (WGS) of bacterial isolates has proven its value to collect relevant information for strain characterization as well as tracing the origin of the contamination by linking the food isolate with the patient's isolate with high resolution. However, the isolation of a bacterial pathogen from food matrices is often time-consuming and not always successful. Therefore, we aimed to improve outbreak investigation by developing a method that can be implemented in reference laboratories to characterize the pathogen in the food vehicle without its prior isolation and link it back to human cases. We tested and validated a shotgun metagenomics approach by spiking food pathogens in specific food matrices using the Shiga toxin-producing Escherichia coli (STEC) as a case study. Different DNA extraction kits and enrichment procedures were investigated to obtain the most practical workflow. We demonstrated the feasibility of shotgun metagenomics to obtain the same information as in ISO/TS 13136:2012 and WGS of the isolate in parallel by inferring the genome of the contaminant and characterizing it in a shorter timeframe. This was achieved in food samples containing different E. coli strains, including a combination of different STEC strains. For the first time, we also managed to link individual strains from a food product to isolates from human cases, demonstrating the power of shotgun metagenomics for rapid outbreak investigation and source tracking.

Mining whole genome sequence data to efficiently attribute individuals to source populations

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.

Four European Salmonella Typhimurium datasets collected to develop WGS-based source attribution methods

Zoonotic Salmonella causes millions of human salmonellosis infections worldwide each year. Information about the source of the bacteria guides risk managers on control and preventive strategies. Source attribution is the effort to quantify the number of sporadic human cases of a specific illness to specific sources and animal reservoirs. Source attribution methods for Salmonella have so far been based on traditional wet-lab typing methods. With the change to whole genome sequencing there is a need to develop new methods for source attribution based on sequencing data. Four European datasets collected in Denmark (DK), Germany (DE), the United Kingdom (UK) and France (FR) are presented in this descriptor. The datasets contain sequenced samples of Salmonella Typhimurium and its monophasic variants isolated from human, food, animal and the environment. The objective of the datasets was either to attribute the human salmonellosis cases to animal reservoirs or to investigate contamination of the environment by attributing the environmental isolates to different animal reservoirs.

Development of food safety risk assessment tools based on molecular typing and WGS of Campylobacter jejuni genome

The ‘learning‐by‐doing’ EU‐FORA fellowship programme in the development of risk assessment tools based on molecular typing and WGS of Campylobacter jejuni genome was structured into two main activities: the primary one focused on training on risk assessment methodology and the secondary one in starting and enhancing the cooperation between the hosting and home organisations, or other joint activities. The primary activities had three subsequent work packages (WPs): WP1 data organisation, WP2 cluster and association analyses, and WP3 development of risk assessment models. The secondary activities have branched into one workshop and the initiation of a cooperation programme between the hosting and home organisations. In the last quarter, the fellow had contributed to the characterisation of some pathogens in possible response to a changing climate, part of the CLEFSA project. The fellow attended various forms of training: online and on‐site courses, and also participated at several conferences and meetings for improving his knowledge and skills, contributing to performing the Campylobacter risk assessment and source attribution.

Integrating Whole-Genome Sequencing Data Into Quantitative Risk Assessment of Foodborne Antimicrobial Resistance: A Review of Opportunities and Challenges

Whole-genome sequencing (WGS) will soon replace traditional phenotypic methods for routine testing of foodborne antimicrobial resistance (AMR). WGS is expected to improve AMR surveillance by providing a greater understanding of the transmission of resistant bacteria and AMR genes throughout the food chain, and therefore support risk assessment activities. At this stage, it is unclear how WGS data can be integrated into quantitative microbial risk assessment (QMRA) models and whether their integration will impact final risk estimates or the assessment of risk mitigation measures. This review explores opportunities and challenges of integrating WGS data into QMRA models that follow the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR. We describe how WGS offers an opportunity to enhance the next-generation of foodborne AMR QMRA modeling. Instead of considering all hazard strains as equally likely to cause disease, WGS data can improve hazard identification by focusing on those strains of highest public health relevance. WGS results can be used to stratify hazards into strains with similar genetic profiles that are expected to behave similarly, e.g., in terms of growth, survival, virulence or response to antimicrobial treatment. The QMRA input distributions can be tailored to each strain accordingly, making it possible to capture the variability in the strains of interest while decreasing the uncertainty in the model. WGS also allows for a more meaningful approach to explore genetic similarity among bacterial populations found at successive stages of the food chain, improving the estimation of the probability and magnitude of exposure to AMR hazards at point of consumption. WGS therefore has the potential to substantially improve the utility of foodborne AMR QMRA models. However, some degree of uncertainty remains in relation to the thresholds of genetic similarity to be used, as well as the degree of correlation between genotypic and phenotypic profiles. The latter could be improved using a functional approach based on prediction of microbial behavior from a combination of 'omics' techniques (e.g., transcriptomics, proteomics and metabolomics). We strongly recommend that methodologies to incorporate WGS data in risk assessment be included in any future revision of the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR.

Use of next-generation sequencing in microbial risk assessment

Despite the ever increase in rigorous control and monitoring measures to assure safe food along the entire farm‐to‐fork chain, the past decade has also witnessed an increase in microbial food alerts. Hence, research on food safety and quality remain of utmost importance. Complementary, and at least as important, is the necessity to be able to assess the potential microbial risks along the food chain. Risk assessment relies on sound scientific data. Unfortunately, often, quality data are limited if not lacking. High‐throughput tools such as next‐generation sequencing (NGS) could fill this gap. NGS approaches can be used to generate ample qualitative and quantitative data to be used in the risk assessment process. NGS applications are not new in food microbiology with applications ranging from pathogen detection along the food chain, food epidemiology studies, whole genome analysis of food‐associated microorganisms up to describing complete food microbiomes. Yet, its application in the area of microbial risk assessment is still at an early stage and faces important challenges. The possibilities of NGS for risk assessment are ample, but so are the questions on the subject. One of the major strengths of NGS lies in its capacity to generate a lot of data, but to what extend can this wealth be of use in hazard identification, hazard characterisation and exposure assessment to perform a sound risk characterisation, which in turn will make it possible to take substantiated risk management decisions.

Phenotypic and Genotypic Eligible Methods for Salmonella Typhimurium Source Tracking

Salmonellosis is one of the most common causes of foodborne infection and a leading cause of human gastroenteritis. Throughout the last decade, Salmonella enterica serotype Typhimurium (ST) has shown an increase report with the simultaneous emergence of multidrug-resistant isolates, as phage type DT104. Therefore, to successfully control this microorganism, it is important to attribute salmonellosis to the exact source. Studies of Salmonella source attribution have been performed to determine the main food/food-production animals involved, toward which, control efforts should be correctly directed. Hence, the election of a ST subtyping method depends on the particular problem that efforts must be directed, the resources and the data available. Generally, before choosing a molecular subtyping, phenotyping approaches such as serotyping, phage typing, and antimicrobial resistance profiling are implemented as a screening of an investigation, and the results are computed using frequency-matching models (i.e., Dutch, Hald and Asymmetric Island models). Actually, due to the advancement of molecular tools as PFGE, MLVA, MLST, CRISPR, and WGS more precise results have been obtained, but even with these technologies, there are still gaps to be elucidated. To address this issue, an important question needs to be answered: what are the currently suitable subtyping methods to source attribute ST. This review presents the most frequently applied subtyping methods used to characterize ST, analyses the major available microbial subtyping attribution models and ponders the use of conventional phenotyping methods, as well as, the most applied genotypic tools in the context of their potential applicability to investigates ST source tracking.

Food-Borne Outbreak Investigation and Molecular Typing: High Diversity of Staphylococcus aureus Strains and Importance of Toxin Detection

Staphylococcus aureus is an important aetiological agent of food intoxications in the European Union as it can cause gastro-enteritis through the production of various staphylococcal enterotoxins (SEs) in foods. Reported enterotoxin dose levels causing food-borne illness are scarce and varying. Three food poisoning outbreaks due to enterotoxin-producing S. aureus strains which occurred in 2013 in Belgium are described. The outbreaks occurred in an elderly home, at a barbecue event and in a kindergarten and involved 28, 18, and six cases, respectively. Various food leftovers contained coagulase positive staphylococci (CPS). Low levels of staphylococcal enterotoxins ranging between 0.015 ng/g and 0.019 ng/g for enterotoxin A (SEA), and corresponding to 0.132 ng/g for SEC were quantified in the food leftovers for two of the reported outbreaks. Molecular typing of human and food isolates using pulsed-field gel electrophoresis (PFGE) and enterotoxin gene typing, confirmed the link between patients and the suspected foodstuffs. This also demonstrated the high diversity of CPS isolates both in the cases and in healthy persons carrying enterotoxin genes encoding emetic SEs for which no detection methods currently exist. For one outbreak, the investigation pointed out to the food handler who transmitted the outbreak strain to the food. Tools to improve staphylococcal food poisoning (SFP) investigations are presented.

Inferring source attribution from a multiyear multisource data set of Salmonella in Minnesota

Salmonella enterica is a global health concern because of its widespread association with foodborne illness. Bayesian models have been developed to attribute the burden of human salmonellosis to specific sources with the ultimate objective of prioritizing intervention strategies. Important considerations of source attribution models include the evaluation of the quality of input data, assessment of whether attribution results logically reflect the data trends and identification of patterns within the data that might explain the detailed contribution of different sources to the disease burden. Here, more than 12,000 non-typhoidal Salmonella isolates from human, bovine, porcine, chicken and turkey sources that originated in Minnesota were analysed. A modified Bayesian source attribution model (available in a dedicated R package), accounting for non-sampled sources of infection, attributed 4,672 human cases to sources assessed here. Most (60%) cases were attributed to chicken, although there was a spike in cases attributed to a non-sampled source in the second half of the study period. Molecular epidemiological analysis methods were used to supplement risk modelling, and a visual attribution application was developed to facilitate data exploration and comprehension of the large multiyear data set assessed here. A large amount of within-source diversity and low similarity between sources was observed, and visual exploration of data provided clues into variations driving the attribution modelling results. Results from this pillared approach provided first attribution estimates for Salmonella in Minnesota and offer an understanding of current data gaps as well as key pathogen population features, such as serotype frequency, similarity and diversity across the sources. Results here will be used to inform policy and management strategies ultimately intended to prevent and control Salmonella infection in the state.

Towards an integrated food safety surveillance system: a simulation study to explore the potential of combining genomic and epidemiological metadata

Foodborne infection is a result of exposure to complex, dynamic food systems. The efficiency of foodborne infection is driven by ongoing shifts in genetic machinery. Next-generation sequencing technologies can provide high-fidelity data about the genetics of a pathogen. However, food safety surveillance systems do not currently provide similar high-fidelity epidemiological metadata to associate with genetic data. As a consequence, it is rarely possible to transform genetic data into actionable knowledge that can be used to genuinely inform risk assessment or prevent outbreaks. Big data approaches are touted as a revolution in decision support, and pose a potentially attractive method for closing the gap between the fidelity of genetic and epidemiological metadata for food safety surveillance. We therefore developed a simple food chain model to investigate the potential benefits of combining 'big' data sources, including both genetic and high-fidelity epidemiological metadata. Our results suggest that, as for any surveillance system, the collected data must be relevant and characterize the important dynamics of a system if we are to properly understand risk: this suggests the need to carefully consider data curation, rather than the more ambitious claims of big data proponents that unstructured and unrelated data sources can be combined to generate consistent insight. Of interest is that the biggest influencers of foodborne infection risk were contamination load and processing temperature, not genotype. This suggests that understanding food chain dynamics would probably more effectively generate insight into foodborne risk than prescribing the hazard in ever more detail in terms of genotype.

Improved traceability of Shiga-toxin-producing Escherichia coli using CRISPRs for detection and typing

Among strains of Shiga-toxin-producing Escherichia coli (STEC), seven serogroups (O26, O45, O103, O111, O121, O145, and O157) are frequently associated with severe clinical illness in humans. The development of methods for their reliable detection from complex samples such as food has been challenging thus far, and is currently based on the PCR detection of the major virulence genes stx1, stx2, and eae, and O-serogroup-specific genes. However, this approach lacks resolution. Moreover, new STEC serotypes are continuously emerging worldwide. For example, in May 2011, strains belonging to the hitherto rarely detected STEC serotype O104:H4 were identified as causative agents of one of the world's largest outbreak of disease with a high incidence of hemorrhagic colitis and hemolytic uremic syndrome in the infected patients. Discriminant typing of pathogens is crucial for epidemiological surveillance and investigations of outbreaks, and especially for tracking and tracing in case of accidental and deliberate contamination of food and water samples. Clustered regularly interspaced short palindromic repeats (CRISPRs) are composed of short, highly conserved DNA repeats separated by unique sequences of similar length. This distinctive sequence signature of CRISPRs can be used for strain typing in several bacterial species including STEC. This review discusses how CRISPRs have recently been used for STEC identification and typing.

Application of Molecular Typing Results in Source Attribution Models: The Case of Multiple Locus Variable Number Tandem Repeat Analysis (MLVA) of Salmonella Isolates Obtained from Integrated Surveillance in Denmark

Salmonella is an important cause of bacterial foodborne infections in Denmark. To identify the main animal-food sources of human salmonellosis, risk managers have relied on a routine application of a microbial subtyping-based source attribution model since 1995. In 2013, multiple locus variable number tandem repeat analysis (MLVA) substituted phage typing as the subtyping method for surveillance of S. Enteritidis and S. Typhimurium isolated from animals, food, and humans in Denmark. The purpose of this study was to develop a modeling approach applying a combination of serovars, MLVA types, and antibiotic resistance profiles for the Salmonella source attribution, and assess the utility of the results for the food safety decisionmakers. Full and simplified MLVA schemes from surveillance data were tested, and model fit and consistency of results were assessed using statistical measures. We conclude that loci schemes STTR5/STTR10/STTR3 for S. Typhimurium and SE9/SE5/SE2/SE1/SE3 for S. Enteritidis can be used in microbial subtyping-based source attribution models. Based on the results, we discuss that an adjustment of the discriminatory level of the subtyping method applied often will be required to fit the purpose of the study and the available data. The issues discussed are also considered highly relevant when applying, e.g., extended multi-locus sequence typing or next-generation sequencing techniques.

World Health Organization Estimates of the Relative Contributions of Food to the Burden of Disease Due to Selected Foodborne Hazards: A Structured Expert Elicitation

BACKGROUND

The Foodborne Disease Burden Epidemiology Reference Group (FERG) was established in 2007 by the World Health Organization (WHO) to estimate the global burden of foodborne diseases (FBDs). This estimation is complicated because most of the hazards causing FBD are not transmitted solely by food; most have several potential exposure routes consisting of transmission from animals, by humans, and via environmental routes including water. This paper describes an expert elicitation study conducted by the FERG Source Attribution Task Force to estimate the relative contribution of food to the global burden of diseases commonly transmitted through the consumption of food.

METHODS AND FINDINGS

We applied structured expert judgment using Cooke's Classical Model to obtain estimates for 14 subregions for the relative contributions of different transmission pathways for eleven diarrheal diseases, seven other infectious diseases and one chemical (lead). Experts were identified through international networks followed by social network sampling. Final selection of experts was based on their experience including international working experience. Enrolled experts were scored on their ability to judge uncertainty accurately and informatively using a series of subject-matter specific 'seed' questions whose answers are unknown to the experts at the time they are interviewed. Trained facilitators elicited the 5th, and 50th and 95th percentile responses to seed questions through telephone interviews. Cooke's Classical Model uses responses to the seed questions to weigh and aggregate expert responses. After this interview, the experts were asked to provide 5th, 50th, and 95th percentile estimates for the 'target' questions regarding disease transmission routes. A total of 72 experts were enrolled in the study. Ten panels were global, meaning that the experts should provide estimates for all 14 subregions, whereas the nine panels were subregional, with experts providing estimates for one or more subregions, depending on their experience in the region. The size of the 19 hazard-specific panels ranged from 6 to 15 persons with several experts serving on more than one panel. Pathogens with animal reservoirs (e.g. non-typhoidal Salmonella spp. and Toxoplasma gondii) were in general assessed by the experts to have a higher proportion of illnesses attributable to food than pathogens with mainly a human reservoir, where human-to-human transmission (e.g. Shigella spp. and Norovirus) or waterborne transmission (e.g. Salmonella Typhi and Vibrio cholerae) were judged to dominate. For many pathogens, the foodborne route was assessed relatively more important in developed subregions than in developing subregions. The main exposure routes for lead varied across subregions, with the foodborne route being assessed most important only in two subregions of the European region.

CONCLUSIONS

For the first time, we present worldwide estimates of the proportion of specific diseases attributable to food and other major transmission routes. These findings are essential for global burden of FBD estimates. While gaps exist, we believe the estimates presented here are the best current source of guidance to support decision makers when allocating resources for control and intervention, and for future research initiatives.

Distribution of Leptospira interrogans by Multispacer Sequence Typing in Urban Norway Rats (Rattus norvegicus): A Survey in France in 2011-2013

BACKGROUND

Urban leptospirosis has increasingly been reported in both developing and developed countries. The control of the disease is limited because our understanding of basic aspects of the epidemiology, including the transmission routes of leptospires among rat populations, remains incomplete. Through the ability to distinguish among Leptospira strains in rats, multispacer sequence typing (MST) could provide a modern understanding of Leptospira epidemiology; however, to our knowledge, the distribution of Leptospira strains among urban rat colonies has not been investigated using MST.

AIMS AND METHODOLOGY

The objective of this study was to identify the Leptospira strains present in rats (Rattus norvegicus) in Lyon (France) using MST and to characterize their spatial distribution. Kidneys and urine were collected from rats trapped live in seven locations in the city and in one suburban location. Each location was considered to represent a rat colony. Bacterial cultures and quantitative polymerase chain reaction (qPCR) assays were performed, and the L. interrogans DNA identified was then genotyped using MST. The distributions of Leptospira strains were spatially described.

KEY RESULTS

Among 84 wild rats, MST profiles were obtained in 35 of 37 rats that had a positive result for L. interrogans by bacterial culture and/or qPCR analyses. All of the MST profiles were related to reference strains previously isolated from human patients that belong to the serogroup Icterohaemorrhagiae and the serovars [strain(s)] Copenhageni [Wijinberg or M20] (n = 26), Icterohaemorrhagiae [CHU Réunion] (n = 7), Icterohaemorrhagiae [R1] (n = 1) and Copenhageni [Shibaura 9] (n = 1). Each colony was infected with leptospires having the same MST profile.

MAJOR CONCLUSIONS

This study demonstrated that MST could be used for the purpose of field studies, either on culture isolates or on DNA extracted from kidneys and urine, to distinguish among L. interrogans isolates in rats. MST could thus be used to monitor their distributions in urban rats from the same city, thereby providing new knowledge that could be applied to explore the circulation of L. interrogans infection in rat colonies. Because the strains are related to those previously found in humans, this application of MST could aid in the source tracking of human leptospirosis, and the findings would be relevant for public health purposes according to the One Health principle.

Outbreak of Salmonella enterica serovar Typhimurium phage type DT41 in Danish poultry production

Salmonella enterica subspecies enterica serovar Typhimurium (S. Typhimurium) is one of the most prevalent serovars in Europe - where both poultry and poultry related products are common sources of human salmonellosis. Due to efficient control programs, the prevalence of S. Typhimurium in Danish poultry production is very low. Despite this, during the past decades there has been a reoccurring problem with infections with S. Typhimurium phage type DT41 in the Danish poultry production without identifying a clear source. In the end of 2013 and beginning of 2014 an increased isolation of S. Typhimurium DT41 was noted mainly in this production, but also in other samples. To investigate this is in more detail, 47 isolates from egg layers (n=5, 1 flock), broilers (n=33, 13 flocks), broiler breeding flocks and hatches (n=5; 2 flocks and 1 environmental hatchery sample), feed (n=1), poultry slaughter house (n=3, environmental sample and meat) were typed with multi locus variable number of tandem repeat analysis (MLVA) and pulsed-field gel electrophoresis (PFGE) to investigate the epidemiology of the outbreak. Based on PFGE results isolates were divided into four groups (Simpson's index of diversity (DI)=0.24±0.15). Due to the low DI, PFGE was not sufficient to provide information to unravel the outbreak. Based on MLVA typing the DT41 (42/47 isolates) and the RDNC isolates (5/47) were split into nine groups (DI=0.65±0.14). When a maximum divergence at one locus was permitted these could be gathered into four groups. Using this criterion, combined with epidemiological information, a spread of one type from broiler breeders to broilers and further to the poultry slaughter house was plausible. In conclusion, although it could be concluded that a spread within the broiler production pyramid had taken place the source of the sudden increase of S. Typhimurium DT41 remains unclear. To investigate this in more detail, further studies using whole genome sequencing to obtain a higher discriminatory strength and including isolates from a longer period of time and from various sources are in progress.

Tracing the sources of human salmonellosis: a multi-model comparison of phenotyping and genotyping methods

Salmonella source attribution is usually performed using frequency-matched models, such as the (modified) Dutch and Hald models, based on phenotyping data, i.e. serotyping, phage typing, and antimicrobial resistance profiling. However, for practical and economic reasons, genotyping methods such as Multi-locus Variable Number of Tandem Repeats Analysis (MLVA) are gradually replacing traditional phenotyping of salmonellas beyond the serovar level. As MLVA-based source attribution of human salmonellosis using frequency-matched models is problematic due to the high variability of the genetic targets investigated, other models need to be explored. Using a comprehensive data set from the Netherlands in 2005-2013, this study aimed at attributing sporadic and domestic cases of Salmonella Typhimurium/4,[5],12:i:- and Salmonella Enteritidis to four putative food-producing animal sources (pigs, cattle, broilers, and layers/eggs) using the modified Dutch and Hald models (based on sero/phage typing data) in comparison with a widely applied population genetics model - the asymmetric island model (AIM) - supplied with MLVA data. This allowed us to compare model outcomes and to corroborate whether MLVA-based Salmonella source attribution using the AIM is able to provide sound, comparable results. All three models provided very similar results, confirming once more that most S. Typhimurium/4,[5],12:i:- and S. Enteritidis cases are attributable to pigs and layers/eggs, respectively. We concluded that MLVA-based source attribution using the AIM is a feasible option, at least for S. Typhimurium/4,[5],12:i:- and S. Enteritidis. Enough information seems to be contained in the MLVA profiles to trace the sources of human salmonellosis even in presence of imperfect temporal overlap between human and source isolates. Besides Salmonella, the AIM might also be applicable to other pathogens that do not always comply to clonal models. This would add further value to current surveillance activities by performing source attribution using genotyping data that are being collected in a standardized fashion internationally.

Salmonella source attribution based on microbial subtyping: does including data on food consumption matter?

Source attribution based on microbial subtyping is being performed in many countries to ascertain the main reservoirs of human salmonellosis and to assess the impact of food safety interventions. To account for differences in exposure, the amount of food available for consumption within a country is often included in Salmonella source attribution models along with the level of contamination. However, not all foods have an equal probability of serving as vehicles for salmonellas, as some foods are more likely to be consumed raw/undercooked than others, posing a relatively higher risk. Using Salmonella data from the Netherlands in 2001-2004, this study aims at elucidating whether and how the incorporation of food consumption data in two source attribution models - the (modified) Dutch and Hald models - affects their attributions. We also propose the incorporation of an additional parameter to weight the amount of food consumed by its likelihood to be consumed raw/undercooked by the population. Incorporating the amount of food consumed caused a drastic change in the ranking of the top reservoirs in the Dutch model, but not in the Hald model, which proved to be insensitive to additional weightings given that its source-dependent factor can account for both food consumption and the ability for foods to serve as vehicles for salmonellas. Compared to attributions without food consumption, the Dutch model including the amount of food consumed showed an increase in the percentage of cases attributable to pigs and a decrease in that of layers/eggs, which became the second reservoir, after pigs. This was not consistent with established knowledge indicating that layers/eggs, rather than pigs, were the main reservoir of human salmonellosis in that period. By incorporating the additional weight reflecting the likelihood for different foods to be consumed raw/undercooked, the attributions of the Dutch model were effectively adjusted, both in terms of ranking and percent contributions of the different reservoirs. We concluded that incorporating food consumption data in the Dutch model can significantly affect the results. Therefore, such data should be either excluded from this model or used together with an additional weight able to adjust the amount of food consumed by its likelihood to be consumed insufficiently cooked. This may help identifying the correct reservoirs, allowing attributions to more closely reflect the real chance for a given food to serve as a vehicle for salmonellas. Conversely, the Hald model works properly irrespective of inclusion of food consumption data.