Matthias Filter

Thermal stability of hepatitis E virus assessed by a molecular biological approach

BackgroundHepatitis E virus (HEV) is a pathogen of emerging concern in industrialized countries. The consumption of wild boar meat has been identified as one risk factor for autochthonous HEV infections. Only limited information is available about thermal stability of HEV, mainly due to the lack of rapid and efficient cell culture systems for measurement of HEV infectivity.MethodsA molecular biological method was implemented in order to distinguish disassembled from intact viral particles using RNase treatment followed by quantitative real-time RT-PCR. The method was applied to a wild boar liver suspension containing HEV genotype 3.ResultsTime-course analyses indicated that the decline of protected RNA could be described by a biphasic model with an initial decrease followed by a stationary phase. The stationary phase was reached after 1 hour at 4°C, 3 days at 22°C and 7 days at 37°C with log reductions of 0.34, 0.45 and 1.24, respectively. Protected RNA was detectable until the end of the experiments at day 50 or 70. Heat exposure for 1 minute resulted in a log reduction of 0.48 at 70°C and increased with higher temperatures to 3.67 at 95°C. Although HEV infectivity titration by inoculation of the liver suspension onto three cell lines did not succeed, the results of the RNase-based method are in accordance with published cell culture-based data.ConclusionsMeasurement of intact viral particles using the RNase-based method may provide data on the stability of RNA viruses when cell culture-based infectivity titrations are not efficient or not available. The method enables processing of large sample numbers and may be suitable to estimate stability of HEV in different types of food.

Survival of Brucella spp. in mineral water, milk and yogurt

Knowledge of the number of organisms in a food product at the time of consumption is crucial to assess the risk from a deliberate contamination of food samples with Brucella. To date, very little data on the survival times of Brucella in different food matrices is available. This study was conducted to assess the survival times of Brucella spp. in water, milk and yogurt. These food products were inoculated with bacteria, serial dilutions of the food samples plated and the number of surviving bacteria counted. Under normal storage conditions Brucella survived in UHT milk for 87 days, for 60 days in water and less than a week in yogurt. Also, when milk was inoculated with low bacterial numbers, Brucella multiplied by five log units within three weeks. Further we could not confirm that a high fat content in food has a protective effect on Brucella survival. Brucella survived in 3.5% and 10.0% fat yogurt for four and two days, respectively. These results show that appropriate methods for the rapid detection of this pathogen from food matrices are required.

FoodChain-Lab: A Trace-Back and Trace-Forward Tool Developed and Applied during Food-Borne Disease Outbreak Investigations in Germany and Europe.

FoodChain-Lab is modular open-source software for trace-back and trace-forward analysis in food-borne disease outbreak investigations. Development of FoodChain-Lab has been driven by a need for appropriate software in several food-related outbreaks in Germany since 2011. The software allows integrated data management, data linkage, enrichment and visualization as well as interactive supply chain analyses. Identification of possible outbreak sources or vehicles is facilitated by calculation of tracing scores for food-handling stations (companies or persons) and food products under investigation. The software also supports consideration of station-specific cross-contamination, analysis of geographical relationships, and topological clustering of the tracing network structure. FoodChain-Lab has been applied successfully in previous outbreak investigations, for example during the 2011 EHEC outbreak and the 2013/14 European hepatitis A outbreak. The software is most useful in complex, multi-area outbreak investigations where epidemiological evidence may be insufficient to discriminate between multiple implicated food products. The automated analysis and visualization components would be of greater value if trading information on food ingredients and compound products was more easily available.

A strategy to establish Food Safety Model Repositories.

Transferring the knowledge of predictive microbiology into real world food manufacturing applications is still a major challenge for the whole food safety modelling community. To facilitate this process, a strategy for creating open, community driven and web-based predictive microbial model repositories is proposed. These collaborative model resources could significantly improve the transfer of knowledge from research into commercial and governmental applications and also increase efficiency, transparency and usability of predictive models. To demonstrate the feasibility, predictive models of Salmonella in beef previously published in the scientific literature were re-implemented using an open source software tool called PMM-Lab. The models were made publicly available in a Food Safety Model Repository within the OpenML for Predictive Modelling in Food community project. Three different approaches were used to create new models in the model repositories: (1) all information relevant for model re-implementation is available in a scientific publication, (2) model parameters can be imported from tabular parameter collections and (3) models have to be generated from experimental data or primary model parameters. All three approaches were demonstrated in the paper. The sample Food Safety Model Repository is available via: http://sourceforge.net/projects/microbialmodelingexchange/files/models and the PMM-Lab software can be downloaded from http://sourceforge.net/projects/pmmlab/. This work also illustrates that a standardized information exchange format for predictive microbial models, as the key component of this strategy, could be established by adoption of resources from the Systems Biology domain.

Development of a Comparative Risk Ranking System for Agents Posing a Bioterrorism Threat to Human or Animal Populations

Various systems for prioritizing biological agents with respect to their applicability as biological weapons are available, ranging from qualitative to (semi)quantitative approaches. This research aimed at generating a generic risk ranking system applicable to human and animal pathogenic agents based on scientific information. Criteria were evaluated and clustered to create a criteria list. Considering availability of data, a number of 28 criteria separated by content were identified that can be classified in 11 thematic areas or categories. Relevant categories contributing to probability were historical aspects, accessibility, production efforts, and possible paths for dispersion. Categories associated with impact are dealing with containment measures, availability of diagnostics, preventive and treatment measures in human and animal populations, impact on society, human and veterinary public health, and economic and ecological consequences. To allow data-based scoring, each criterion was described by at least 1 measure that allows the assignment of values. These values constitute quantities, ranges, or facts that are as explicit and precise as possible. The consideration of minimum and maximum values that can occur due to natural variations and that are often described in the literature led to the development of minimum and maximum criteria and consequently category scores. Missing or incomplete data, and uncertainty resulting therefrom, were integrated into the scheme via a cautious (but not overcautious) approach. The visualization technique that was used allows the description and illustration of uncertainty on the level of probability and impact. The developed risk ranking system was evaluated by assessing the risk originating from the bioterrorism threat of the animal pathogen bluetongue virus, the human pathogen Enterohemorrhagic Escherichia coli O157:H7, the zoonotic Bacillus anthracis, and Botulinum neurotoxin.

Accelerating investigation of food-borne disease outbreaks using pro-active geospatial modeling of food supply chains

Over the last decades the globalization of trade has significantly altered the topology of food supply chains. Even though food-borne illness has been consistently on the decline, the hazardous impact of contamination events is larger [1-3]. Possible contaminants include pathogenic bacteria, viruses, parasites, toxins or chemicals. Contamination can occur accidentally, e.g. due to improper handling, preparation, or storage, or intentionally as the melamine milk crisis proved. To identify the source of a food-borne disease it is often necessary to reconstruct the food distribution networks spanning different distribution channels or product groups. The time needed to trace back the contamination source ranges from days to weeks and significantly influences the economic and public health impact of a disease outbreak. In this paper we describe a model-based approach designed to speed up the identification of a food-borne disease outbreak source. Further, we exploit the geospatial information of wholesaler-retailer food distribution networks limited to a given food type and apply a gravity model for food distribution from retailer to consumer. We present a likelihood framework that allows determining the likelihood of wholesale source(s) distributing contaminated food based on geo-coded case reports. The developed method is independent of the underlying food distribution kernel and thus particularly applicable to empirical distributions of food acquisition.

Discussing State-of-the-Art Spatial Visualization Techniques Applicable for the Epidemiological Surveillance Data on the Example of Campylobacter spp. in Raw Chicken Meat

Within the European activities for the ‘Monitoring and Collection of Information on Zoonoses’, annually EFSA publishes a European report, including information related to the prevalence of Campylobacter spp. in Germany. Spatial epidemiology becomes here a fundamental tool for the generation of these reports, including the representation of prevalence as an essential element. Until now, choropleth maps are the default visualization technique applied in epidemiological monitoring and surveillance reports made by EFSA and German authorities. However, due to its limitations, it seems to be reasonable to explore alternative chart type. Four maps including choropleth, cartogram, graduated symbols and dot‐density maps were created to visualize real‐world sample data on the prevalence of Campylobacter spp. in raw chicken meat samples in Germany in 2011. In addition, adjacent and coincident maps were created to visualize also the associated uncertainty. As an outcome, we found that there is not a single data visualization technique that encompasses all the necessary features to visualize prevalence data alone or prevalence data together with their associated uncertainty. All the visualization techniques contemplated in this study demonstrated to have both advantages and disadvantages. To determine which visualization technique should be used for future reports, we recommend to create a dialogue between end‐users and epidemiologists on the basis of sample data and charts. The final decision should also consider the knowledge and experience of end‐users as well as the specific objective to be achieved with the charts.

An Open-Source Community Resource for Creating, Collecting, Sharing and Applying Predictive Microbial Models (PMM-Lab)

Quantitative microbiological risk assessments (QMRA) in the farm-to-fork continuum heavily rely on mathematical models for growth, survival and inactivation of micro-organisms in different food matrices and processing conditions, collectively subsumed under the heading “predictive microbial models” (PMM). Unfortunately, the currently publicly available PMM are characterized by a great heterogeneity with respect to applicability, quality, validity, documentation, application limits and software requirements.

Exploitation of Commercial B2B Data for Risk Assessment Tasks in Foodborne Crisis Events

In foodborne crisis events timely and scientifically sound risk assessments are of utmost relevance for all stakeholders. Risk assessments serve as guidance for governmental and private sector crisis management and affect international trade relationships as well. Therefore it is necessary to continuously explore data sources and data analysis technologies which support the risk assessment process.

Towards a Food Safety Knowledge Base Applicable in Crisis Situations and Beyond

In case of contamination in the food chain, fast action is required in order to reduce the numbers of affected people. In such situations, being able to predict the fate of agents in foods would help risk assessors and decision makers in assessing the potential effects of a specific contamination event and thus enable them to deduce the appropriate mitigation measures. One efficient strategy supporting this is using model based simulations. However, application in crisis situations requires ready-to-use and easy-to-adapt models to be available from the so-called food safety knowledge bases. Here, we illustrate this concept and its benefits by applying the modular open source software tools PMM-Lab and FoodProcess-Lab. As a fictitious sample scenario, an intentional ricin contamination at a beef salami production facility was modelled. Predictive models describing the inactivation of ricin were reviewed, relevant models were implemented with PMM-Lab, and simulations on residual toxin amounts in the final product were performed with FoodProcess-Lab. Due to the generic and modular modelling concept implemented in these tools, they can be applied to simulate virtually any food safety contamination scenario. Apart from the application in crisis situations, the food safety knowledge base concept will also be useful in food quality and safety investigations.