Screening of Swiss Pig Herds for Hepatitis E Virus: A Pilot Study

Hepatitis E virus (HEV) is an important cause of acute hepatitis in humans worldwide. In industrialised countries, most infections are caused by the zoonotic genotype 3. The main reservoir was found in pigs, with fattening pigs as the main shedders. The aim of this study was to establish a screening tool to detect HEV in pig farms. HEV-positive samples were sequenced using Sanger sequencing. First, different sample materials, including floor swabs, slurry, dust swabs and faeces were tested for HEV. Floor swabs turned out to give the best results and, in the form of sock swabs, were used for the screening of Swiss pig herds. A total of 138 pig farms were tested, with a focus on fattening pigs. Overall, 81 farms (58.8%) were HEV positive. Most sequences belonged to subtype 3h, in which they formed a specific cluster (Swiss cluster). In addition, subtype 3 l and two unassigned sequences were detected. As a conclusion, sock swabs were found to be a helpful tool to screen pig herds for HEV and establish a sequence collection that may enable molecular epidemiology and support outbreak investigation and prevention.

Barriers and Levers to Developing Wheat–Pea Intercropping in Europe: A Review

Beyond the ecosystem benefits of diversification through wheat–pea intercropping, this review analyzes the barriers and levers to its adoption and diffusion. The present review shows that structuring the value chain around the products of this innovative cropping system faces a set of technical (i.e., varietal selection, phytosanitary issue control, crop management sequence, collection management, and storage), economic (i.e., cost, price, market opportunities, and contracting), and public policy (i.e., subsidies for ecosystem services provided by intercropping) obstacles that contribute to its slow adoption and dissemination in Europe. However, the value chain resulting from the wheat–pea intercropping system has levers to be exploited at all levels, particularly in terms of its competitive advantages, ecosystem benefits, and superior product quality. The results of this review help to identify priorities that actors of the value chain can address to better focus their efforts on significant problems and solutions that can accelerate the adoption and dissemination of this agroecological system.

Metabarcoding of soil nematodes: the importance of taxonomic coverage and availability of reference sequences in choosing suitable marker(s)

For many organisms, there is agreement on the specific genomic region used for developing barcode markers. With nematodes, however, it has been found that the COI region designated for most animals lacks the taxonomic coverage (ability to amplify a diverse group of taxa) required of a metabarcoding marker. For that reason, studies on metabarcoding of nematodes thus far have utilized primarily regions within the highly conserved 18S ribosomal DNA. Two popular markers within this region are the ones flanked by the primer pairs NF1-18Sr2b and SSUF04-SSUR22. The NF1-18Sr2b primer pair, especially, has been critiqued as not being specific enough for nematodes leading to suggestions for other candidate markers while the SSUF04-SSUR22 region has hardly been tested on soil nematodes. The current study aimed to evaluate these two markers against other alternative ones within the 28S rDNA and the COI region for their suitability for nematode metabarcoding. The results showed that the NF1-18Sr2b marker could offer wide coverage and good resolution for characterizing soil nematodes. Sufficient availability of reference sequences for this region was found to be a significant factor that resulted in this marker outperforming the other markers, particularly the 18S-based SSUFO4-SSUR22 marker. None of the other tested regions compared with this marker in terms of the proportion of the taxa recovered. The COI-based marker had the lowest number of taxa recovered, and this was due to the poor performance of its primers and the insufficient number of reference sequences in public databases. In summary, this study highlights how dependent the success of metabarcoding is on the availability of a good reference sequence collection for the marker of choice as well as its taxonomic coverage.

Quantifying Pathogen Surveillance Using Temporal Genomic Data

ABSTRACTWith the advent of deep sequencing, genomic surveillance has become a popular method for detection of infectious disease, supplementing information gathered by classic clinical or serological techniques to identify host-determinant markers and trace the origin of transmission. However, two main factors complicate genomic surveillance. First, pathogens exhibiting high genetic diversity demand higher levels of scrutiny to obtain an accurate representation of the entire population. Second, current systems of detection are nonuniform, with significant gaps in certain geographic locations and animal reservoirs. Despite past unforeseen pandemics like the 2009 swine-origin H1N1 influenza virus, there is no standardized way of evaluating surveillance. A more complete surveillance system should capture a greater proportion of pathogen diversity. Here we present a novel quantitative method of assessing the completeness of genomic surveillance that incorporates the time of sequence collection, as well as the pathogen’s evolutionary rate. We propose theq2 coefficient, which measures the proportion of sequenced isolates whose closest neighbor in the past is within a genetic distance equivalent to 2 years of evolution, roughly the median time of changing strain selection for influenza A vaccines. Easily interpretable and significantly faster than other methods, theq2 coefficient requires no full phylogenetic characterization or use of arbitrary clade definitions. Application of theq2 coefficient to influenza A virus confirmed poor sampling of swine and avian populations and identified regions with deficient surveillance. We demonstrate that theq2 coefficient can not only be applied to other pathogens, including dengue and West Nile viruses, but also used to describe surveillance dynamics, particularly the effects of different public health policies.IMPORTANCESurveillance programs have become key assets in determining the emergence or prevalence of pathogens circulating in human and animal populations. Genomic surveillance, in particular, provides comprehensive information on the history of isolates and potential molecular markers for infectivity and pathogenicity. Current techniques for evaluating genomic surveillance are inaccurate, ignoring the pathogen’s evolutionary rate and biodiversity, as well as the timing of sequence collection. Using sequence data, we propose theq2 coefficient as a quantitative measure of surveillance completeness that combines elements of time and evolution without defining arbitrary criteria for clades or species. Through several case studies of influenza A, dengue, and West Nile viruses, we employed theq2 coefficient to identify sampling deficiencies in different host species and locations, as well as examine the effects of different public health policies through historical records of theq2 coefficient. These results can guide public health agencies to focus resource allocation and virus collection to bolster specific problems in surveillance.