The COMPARE Data Hubs.

Data sharing enables research communities to exchange findings and build upon the knowledge that arises from their discoveries. Areas of public and animal health as well as food safety would benefit from rapid data sharing when it comes to emergencies. However, ethical, regulatory and institutional challenges, as well as lack of suitable platforms which provide an infrastructure for data sharing in structured formats, often lead to data not being shared or at most shared in form of supplementary materials in journal publications. Here, we describe an informatics platform that includes workflows for structured data storage, managing and pre-publication sharing of pathogen sequencing data and its analysis interpretations with relevant stakeholders.

Metagenomics for broad and improved parasite detection: a proof-of-concept study using swine faecal samples.

Efficient and reliable identification of emerging pathogens is crucial for the design and implementation of timely and proportionate control strategies. This is difficult if the pathogen is so far unknown or only distantly related with known pathogens. Diagnostic metagenomics - an undirected, broad and sensitive method for the efficient identification of pathogens - was frequently used for virus and bacteria detection, but seldom applied to parasite identification. Here, metagenomics datasets prepared from swine faeces using an unbiased sample processing approach with RNA serving as starting material were re-analysed with respect to parasite detection. The taxonomic identification tool RIEMS, used for initial detection, provided basic hints on potential pathogens contained in the datasets. The suspected parasites/intestinal protists (Blastocystis, Entamoeba, Iodamoeba, Neobalantidium, Tetratrichomonas) were verified using subsequently applied reference mapping analyses on the base of rRNA sequences. Nearly full-length gene sequences could be extracted from the RNA-derived datasets. In the case of Blastocystis, subtyping was possible with subtype (ST)15 discovered for the first known time in swine faeces. Using RIEMS, some of the suspected candidates turned out to be false-positives caused by the poor status of sequences in publicly available databases. Altogether, 11 different species/STs of parasites/intestinal protists were detected in 34 out of 41 datasets extracted from metagenomics data. The approach operates without any primer bias that typically hampers the analysis of amplicon-based approaches, and allows the detection and taxonomic classification including subtyping of protist and metazoan endobionts (parasites, commensals or mutualists) based on an abundant biomarker, the 18S rRNA. The generic nature of the approach also allows evaluation of interdependencies that induce mutualistic or pathogenic effects that are often not clear for many intestinal protists and perhaps other parasites. Thus, metagenomics has the potential for generic pathogen identification beyond the characterisation of viruses and bacteria when starting from RNA instead of DNA.

Proficiency Testing of Virus Diagnostics Based on Bioinformatics Analysis of Simulated In Silico High-Throughput Sequencing Data Sets.

Quality management and independent assessment of high-throughput sequencing-based virus diagnostics have not yet been established as a mandatory approach for ensuring comparable results. The sensitivity and specificity of viral high-throughput sequence data analysis are highly affected by bioinformatics processing using publicly available and custom tools and databases and thus differ widely between individuals and institutions. Here we present the results of the COMPARE [Collaborative Management Platform for Detection and Analyses of (Re-)emerging and Foodborne Outbreaks in Europe] in silico virus proficiency test. An artificial, simulated in silico data set of Illumina HiSeq sequences was provided to 13 different European institutes for bioinformatics analysis to identify viral pathogens in high-throughput sequence data. Comparison of the participants' analyses shows that the use of different tools, programs, and databases for bioinformatics analyses can impact the correct identification of viral sequences from a simple data set. The identification of slightly mutated and highly divergent virus genomes has been shown to be most challenging. Furthermore, the interpretation of the results, together with a fictitious case report, by the participants showed that in addition to the bioinformatics analysis, the virological evaluation of the results can be important in clinical settings. External quality assessment and proficiency testing should become an important part of validating high-throughput sequencing-based virus diagnostics and could improve the harmonization, comparability, and reproducibility of results. There is a need for the establishment of international proficiency testing, like that established for conventional laboratory tests such as PCR, for bioinformatics pipelines and the interpretation of such results.

LVQ-KNN: Composition-based DNA/RNA binning of short nucleotide sequences utilizing a prototype-based k-nearest neighbor approach.

Unbiased sequencing is an upcoming method to gain information of the microbiome in a sample and for the detection of unrecognized pathogens. There are many software tools for a taxonomic classification of such metagenomics datasets available. Numerous of them have a satisfactory sensitivity and specificity for known organisms, but they fail if the sample contains unknown organisms, which cannot be detected by similarity-based classification employing available databases. However, recognition of unknowns is especially important for the detection of newly emerging pathogens, which are often RNA viruses. Here we present the composition-based analysis tool LVQ-KNN for binning unclassified nucleotide sequence reads into their provenance classes DNA or RNA. With a 5-fold cross-validation, LVQ-KNN reached correct classification rates (CCR) of up to 99.9% for the classification into DNA/RNA. Real datasets gained CCRs of up to 94.5%. Comparing the method to another composition-based analysis tool, similar or better classification results were reached. LVQ-KNN is a new tool for DNA/RNA classification of sequence reads from unbiased sequencing approaches that could be applicable for the detection of yet unknown RNA viruses in metagenomic samples. The source-code, training and test data for LVQ-KNN is available at Github (https://github.com/ab1989/LVQ-KNN).

The Accessory Genome of Shiga Toxin-Producing Escherichia coli Defines a Persistent Colonization Type in Cattle.

UNLABELLED: Shiga toxin-producing Escherichia coli (STEC) strains can colonize cattle for several months and may, thus, serve as gene reservoirs for the genesis of highly virulent zoonotic enterohemorrhagic E. coli (EHEC). Attempts to reduce the human risk for acquiring EHEC infections should include strategies to control such STEC strains persisting in cattle. We therefore aimed to identify genetic patterns associated with the STEC colonization type in the bovine host. We included 88 persistent colonizing STEC (STEC(per)) (shedding for ≥4 months) and 74 sporadically colonizing STEC (STEC(spo)) (shedding for ≤2 months) isolates from cattle and 16 bovine STEC isolates with unknown colonization types. Genoserotypes and multilocus sequence types (MLSTs) were determined, and the isolates were probed with a DNA microarray for virulence-associated genes (VAGs). All STEC(per) isolates belonged to only four genoserotypes (O26:H11, O156:H25, O165:H25, O182:H25), which formed three genetic clusters (ST21/396/1705, ST300/688, ST119). In contrast, STEC(spo) isolates were scattered among 28 genoserotypes and 30 MLSTs, with O157:H7 (ST11) and O6:H49 (ST1079) being the most prevalent. The microarray analysis identified 139 unique gene patterns that clustered with the genoserotypes and MLSTs of the strains. While the STEC(per) isolates possessed heterogeneous phylogenetic backgrounds, the accessory genome clustered these isolates together, separating them from the STEC(spo) isolates. Given the vast genetic heterogeneity of bovine STEC strains, defining the genetic patterns distinguishing STEC(per) from STEC(spo) isolates will facilitate the targeted design of new intervention strategies to counteract these zoonotic pathogens at the farm level. IMPORTANCE: Ruminants, especially cattle, are sources of food-borne infections by Shiga toxin-producing Escherichia coli (STEC) in humans. Some STEC strains persist in cattle for longer periods of time, while others are detected only sporadically. Persisting strains can serve as gene reservoirs that supply E. coli with virulence factors, thereby generating new outbreak strains. Attempts to reduce the human risk for acquiring STEC infections should therefore include strategies to control such persisting STEC strains. By analyzing representative genes of their core and accessory genomes, we show that bovine STEC with a persistent colonization type emerged independently from sporadically colonizing isolates and evolved in parallel evolutionary branches. However, persistent colonizing strains share similar sets of accessory genes. Defining the genetic patterns that distinguish persistent from sporadically colonizing STEC isolates will facilitate the targeted design of new intervention strategies to counteract these zoonotic pathogens at the farm level.