Ergebnisse, Schlussfolgerungen und Empfehlungen aus zwei Ringversuchen zum Nachweis und zur Isolierung von Shiga (Vero) Toxin bildenden Escherichia coli (STEC) aus Hackfleischproben

Using hydrochloric acid and bile resistance for optimized detection and isolation of Shiga toxin-producing Escherichia coli (STEC) from sprouts

Shiga toxin-producing Escherichia coli (STEC) in sprouts have caused large scale outbreaks in the past involving severe illness. The combination of this very diverse pathogen and a food matrix with high numbers of background microbiota poses a particular challenge for detection and isolation. An acid treatment of the enrichment before plating on agar has been shown to improve the recovery of STEC from sprouts. After enrichment in buffered peptone water (BPW) at 37 °C we applied an acid treatment, followed by plating on tryptone bile x-glucuronide (TBX) agar (acid bile method). An inter-laboratory study was organized with 21 laboratories taking part to evaluate the performance parameters and applicability of the acid bile method. A sample set of six sprout samples was prepared consisting of two uninoculated samples and four spiked samples, each containing one of two STEC strains at one of two concentrations (low and high). Analyzing a set of six samples at the National Reference Laboratory (NRL E. coli), we determined the relative abundance of STEC without, after acid-, after bile- and after acid-bile treatment using real-time PCR. The participating laboratories successfully applied the acid bile method and were better able to detect (sensitivity 92.9% vs. 70.0%) and isolate (sensitivity 87.5% vs. 31.3%) STEC from positive samples using the acid bile method compared to non-acid methods. The relative limit of detection (RLOD) after isolation using the acid bile method (vs. non-acid method) was <1 for both STEC strains used, BfR-EC-14434 O133:H25 (0.146) and BfR-EC-16015 O26:H11 (0.073). A collection of STEC (n = 71) of diverse type and characteristics was assessed for their resistance towards the acid bile treatment selection. The majority (n = 65) of STEC strains could be recovered after acid treatment on TBX plates. However, a few strains (n = 6), among them clinical isolates were (partly) sensitive. These results suggest that an acid bile method is a rapid and reasonable approach to improve the recovery of STEC from sprouts when used in combination with methods targeting other selection markers.

Detection of Shiga Toxin-Producing Escherichia coli from Nonhuman Sources and Strain Typing

Shiga toxin-producing Escherichia coli (STEC) strains are commonly found in the intestine of ruminant species of wild and domestic animals. Excretion of STEC with animal feces results in a broad contamination of food and the environment. Humans get infected with STEC through ingestion of contaminated food, by contact with the environment, and from STEC-excreting animals and humans. STEC strains can behave as human pathogens, and some of them, called enterohemorrhagic E. coli (EHEC), may cause hemorrhagic colitis (HC) and hemolytic-uremic syndrome (HUS). Because of the diversity of STEC types, detection strategies for STEC and EHEC are based on the identification of Shiga toxins or the underlying genes. Cultural enrichment of STEC from test samples is needed for identification, and different protocols were developed for this purpose. Multiplex real-time PCR protocols (ISO/CEN TS13136 and USDA/FSIS MLG5B.01) have been developed to specifically identify EHEC by targeting the LEE (locus of enterocyte effacement)-encoded eae gene and genes for EHEC-associated O groups. The employment of more genetic markers (nle and CRISPR) is a future challenge for better identification of EHEC from any kinds of samples. The isolation of STEC or EHEC from a sample is required for confirmation, and different cultivation protocols and media for this purpose have been developed. Most STEC strains present in food, animals, and the environment are eae negative, but some of these strains can cause HC and HUS in humans as well. Phenotypic assays and molecular tools for typing EHEC and STEC strains are used to detect and characterize human pathogenic strains among members of the STEC group.

Graphical models and Bayesian domains in risk modelling: application in microbiological risk assessment

Quantitative microbiological risk assessment (QMRA) models are used to reflect knowledge about complex real-world scenarios for the propagation of microbiological hazards along the feed and food chain. The aim is to provide insight into interdependencies among model parameters, typically with an interest to characterise the effect of risk mitigation measures. A particular requirement is to achieve clarity about the reliability of conclusions from the model in the presence of uncertainty. To this end, Monte Carlo (MC) simulation modelling has become a standard in so-called probabilistic risk assessment. In this paper, we elaborate on the application of Bayesian computational statistics in the context of QMRA. It is useful to explore the analogy between MC modelling and Bayesian inference (BI). This pertains in particular to the procedures for deriving prior distributions for model parameters. We illustrate using a simple example that the inability to cope with feedback among model parameters is a major limitation of MC modelling. However, BI models can be easily integrated into MC modelling to overcome this limitation. We refer a BI submodel integrated into a MC model to as a "Bayes domain". We also demonstrate that an entire QMRA model can be formulated as Bayesian graphical model (BGM) and discuss the advantages of this approach. Finally, we show example graphs of MC, BI and BGM models, highlighting the similarities among the three approaches.