NAuRA: Genomic Tool to Identify Staphylococcal Enterotoxins in Staphylococcus aureus Strains Responsible for FoodBorne Outbreaks.

Food contamination by staphylococcal enterotoxins (SEs) is responsible for many food poisoning outbreaks (FPOs) each year, and they represent the third leading cause of FPOs in Europe. SEs constitute a protein family with 27 proteins. However, enzyme immunoassays can only detect directly in food the five classical SEs (SEA-SEE). Thus, molecular characterization methods of strains found in food are now used for FPO investigations. Here, we describe the development and implementation of a genomic analysis tool called NAuRA (Nice automatic Research of alleles) that can detect the presence of 27 SEs genes in just one analysis- and create a database of allelic data and protein variants for harmonizing analyses. This tool uses genome assembly data and the 27 protein sequences of SEs. To include the different divergence levels between SE-coding genes, parameters of coverage and identity were generated from 10,000 simulations and a dataset of 244 assembled genomes from strains responsible for outbreaks in Europe as well as the RefSeq reference database. Based on phylogenetic inference performed using maximum-likelihood on the core genomes of the strains in this collection, we demonstrated that strains responsible for FPOs are distributed throughout the phylogenetic tree. Moreover, 71 toxin profiles were obtained using the NAuRA pipeline and these profiles do not follow the evolutionary history of strains. This study presents a pioneering method to investigate strains isolated from food at the genomic level and to analyze the diversity of all 27 SE-coding genes together.

Integrated analysis of established and novel microbial and chemical methods for microbial source tracking.

Several microbes and chemicals have been considered as potential tracers to identify fecal sources in the environment. However, to date, no one approach has been shown to accurately identify the origins of fecal pollution in aquatic environments. In this multilaboratory study, different microbial and chemical indicators were analyzed in order to distinguish human fecal sources from nonhuman fecal sources using wastewaters and slurries from diverse geographical areas within Europe. Twenty-six parameters, which were later combined to form derived variables for statistical analyses, were obtained by performing methods that were achievable in all the participant laboratories: enumeration of fecal coliform bacteria, enterococci, clostridia, somatic coliphages, F-specific RNA phages, bacteriophages infecting Bacteroides fragilis RYC2056 and Bacteroides thetaiotaomicron GA17, and total and sorbitol-fermenting bifidobacteria; genotyping of F-specific RNA phages; biochemical phenotyping of fecal coliform bacteria and enterococci using miniaturized tests; specific detection of Bifidobacterium adolescentis and Bifidobacterium dentium; and measurement of four fecal sterols. A number of potentially useful source indicators were detected (bacteriophages infecting B. thetaiotaomicron, certain genotypes of F-specific bacteriophages, sorbitol-fermenting bifidobacteria, 24-ethylcoprostanol, and epycoprostanol), although no one source identifier alone provided 100% correct classification of the fecal source. Subsequently, 38 variables (both single and derived) were defined from the measured microbial and chemical parameters in order to find the best subset of variables to develop predictive models using the lowest possible number of measured parameters. To this end, several statistical or machine learning methods were evaluated and provided two successful predictive models based on just two variables, giving 100% correct classification: the ratio of the densities of somatic coliphages and phages infecting Bacteroides thetaiotaomicron to the density of somatic coliphages and the ratio of the densities of fecal coliform bacteria and phages infecting Bacteroides thetaiotaomicron to the density of fecal coliform bacteria. Other models with high rates of correct classification were developed, but in these cases, higher numbers of variables were required.

Tracking the origin of faecal pollution in surface water: an ongoing project within the European Union research programme.

The objectives of this study are to generate knowledge about methods to track the sources of faecal pollution in surface waters, with the aim of having one or a few easy procedures applicable to different geographic areas in Europe. For this, a first field study using already proposed methods (genotypes of F-specific RNA bacteriophages, bacteriophages infecting Bacteroides fragilis, phenotypes of faecal coliforms and enterococci, and sterols) has been done in five areas representing a wide array of conditions in Europe. The present faecal indicators (faecal coliforms, enterococci, sulfite reducing clostridia and somatic coliphages) have also been included in this first field study. At the same time some emerging methods have been settled or adapted to water samples and assayed in a limited number of samples. The results of this first field study indicate that no single parameter alone is able to discriminate the sources, human or non-human, of faecal pollution, but that a 'basket' of 4 or 5 parameters, which includes one of the present faecal indicators, will do so. In addition, numerical analysis of the data shows that this 'basket' will allow the successful building of predictive models. Both the statistical analyses and the studied predictive models indicate that genotype II of F-specific RNA bacteriophages, the coprostanol and the ratio coprostanol: coprostanol+epicoprostanol are, out of the studied parameters, those with a greater discriminating power. Either because unsuccessful adaptation of the methods to water samples or because the preliminary assays in water samples indicated low discriminating capability, only three (sorbitol-fermenting bifidobacteria, some species of bifidobacteria detected by PCR with specific primers and phages infecting Bacteroides tethaiotaomicron) of the newly assayed methods have been considered for a second field study, which is currently underway. Expectations are that these new tools will minimize the number of parameters in the 'basket', or at least minimize the difficulty in assaying them.