Marta Hernández

Numerous viruses of human or animal origin can spread in the environment and infect people via water and food, mostly through ingestion and occasionally through skin contact. These viruses are released into the environment by various routes including water run-offs and aerosols. Furthermore, zoonotic viruses may infect humans exposed to contaminated surface waters. Foodstuffs of animal origin can be contaminated, and their consumption may cause human infection if the viruses are not inactivated during food processing. Molecular epidemiology and surveillance of environmental samples are necessary to elucidate the public health hazards associated with exposure to environmental viruses. Whereas monitoring of viral nucleic acids by PCR methods is relatively straightforward and well documented, detection of infectious virus particles is technically more demanding and not always possible (e.g. human norovirus or hepatitis E virus). The human pathogenic viruses that are most relevant in this context are nonenveloped and belong to the families of the Caliciviridae, Adenoviridae, Hepeviridae, Picornaviridae and Reoviridae. Sampling methods and strategies, first-choice detection methods and evaluation criteria are reviewed.

We developed and assessed real-time PCR (RTi-PCR) assays for the detection and quantification of the food-borne pathogen Listeria monocytogenes and the closely related nonpathogenic species L. innocua. The target genes were hly and iap for L. monocytogenes and lin02483 for L. innocua. The assays were 100% specific, as determined with 100 Listeria strains and 45 non-Listeria strains, and highly sensitive, with detection limits of one target molecule in 11 to 56% of the reactions with purified DNA and 3 CFU in 56 to 89% of the reactions with bacterial suspensions. Quantification was possible over a 5-log dynamic range, with a limit of 15 target molecules and R(2) values of >0.996. There was an excellent correspondence between the predicted and the actual numbers of CFU in the samples (deviations of <23%). The hly-based assay accurately quantified L. monocytogenes in all of the samples tested. The iap-based assay, in contrast, was unsuitable for quantification purposes, underestimating the bacterial counts by 3 to 4 log units in a significant proportion of the samples due to serovar-related target sequence variability. The combination of the two assays enabled us to classify L. monocytogenes isolates into one of the two major phylogenetic divisions of the species, I and II. We also assessed the new AmpliFluor technology for the quantitative detection of L. monocytogenes by RTi-PCR. The performance of this system was similar to that of the TaqMan system, although the former system was slightly less sensitive (detection limit of 15 molecules in 45% of the reactions) and had a higher quantification limit (60 molecules).

MOTIVATION: The progress of High Throughput Sequencing (HTS) technologies and the reduction in the sequencing costs are such that Whole Genome Sequencing (WGS) could replace many traditional laboratory assays and procedures. Exploiting the volume of data produced by HTS platforms requires substantial computing skills and this is the main bottleneck in the implementation of WGS as a routine laboratory technique. The way in which the vast amount of results are presented to researchers and clinicians with no specialist knowledge of genome sequencing is also a significant issue. RESULTS: Here we present TORMES, a user-friendly pipeline for WGS analysis of bacteria from any origin generated by HTS on Illumina platforms. TORMES is designed for non-bioinformatician users, and automates the steps required for WGS analysis directly from the raw sequence data: sequence quality filtering, de novo assembly, draft genome ordering against a reference, genome annotation, multi-locus sequence typing (MLST), searching for antibiotic resistance and virulence genes, and pangenome comparisons. Once the analysis is finished, TORMES generates and interactive web-like report that can be opened in any web browser and shared and revised by researchers in a simple manner. TORMES can be run by using very simple commands and represent a quick an easy way to perform WGS analysis. AVAILABILITY AND IMPLEMENTATION: TORMES is free available at https://github.com/nmquijada/tormes. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.