Diagnostic Performances of Different Genome Amplification Assays for the Detection of Swine Vesicular Disease Virus in Relation to Genomic Lineages That Circulated in Italy

Advances in the differential molecular diagnosis of vesicular disease pathogens in swine

Foot-and-mouth disease virus (FMDV), Senecavirus A (SVA) and swine vesicular disease virus (SVDV) are members of the family Picornaviridae, which can cause similar symptoms - vesicular lesions in the tissues of the mouth, nose, feet, skin and mucous membrane of animals. Rapid and accurate diagnosis of these viruses allows for control measures to prevent the spread of these diseases. Reverse transcription-polymerase chain reaction (RT-PCR) and real-time RT-PCR are traditional and reliable methods for pathogen detection, while their amplification reaction requires a thermocycler. Isothermal amplification methods including loop-mediated isothermal amplification and recombinase polymerase amplification developed in recent years are simple, rapid and do not require specialized equipment, allowing for point of care diagnostics. Luminex technology allows for simultaneous detection of multiple pathogens. CRISPR-Cas diagnostic systems also emerging nucleic acid detection technologies which are very sensitivity and specificity. In this paper, various nucleic acid detection methods aimed at vesicular disease pathogens in swine (including FMDV, SVA and SVDV) are summarized.

Validation of a competitive enzyme-linked immunosorbent assay to improve the serological diagnosis of swine vesicular disease

Swine vesicular disease (SVD) is an infectious viral disease of pigs. The clinical symptoms of SVD are indistinguishable from other vesicular diseases. In countries free of vesicular diseases, rapid SVD diagnosis and differentiation from other vesicular diseases are essential. In this report, a competitive enzyme-linked immunosorbent assay (cELISA) was developed and validated to improve the current SVD serological diagnosis. In this cELISA, an anti-SVD monoclonal antibody (mAb) captures the recombinant SVD virus-like particle (SVD-VLP) antigen, and 5B7 mAb is used as a competitor to compete with polyclonal antibodies in SVD-positive sera. The cut-off value of the SVD-VLP based cELISA (SVD-VLP cELISA) is ≥ 65% inhibition (%). The determined diagnostic specificity was 99.2%. SVD-VLP cELISA successfully detected SVD antibodies in the sera of SVD-infected animals and produced a diagnostic sensitivity of 100%. A panel of SVD positive sere including outbreak samples (n = 11) and samples (n = 5) from experimentally inoculated pigs, were correctly identified as positive by the SVD-VLP cELISA. In terms of reducing false positives detected by the currently used cELISA (5B7 cELISA), the performance of SVD-VLP cELISA is comparable to the gold standard virus neutralization test.

Multiple detection and spread of novel strains of the SARS-CoV-2 B.1.177 (B.1.177.75) lineage that test negative by a commercially available nucleocapsid gene real-time RT-PCR

Several lineages of SARS-CoV-2 are currently circulating worldwide. During SARS-CoV-2 diagnostic activities performed in Abruzzo region (central Italy) several strains belonging to the B.1.177.75 lineage tested negative for the N gene but positive for the ORF1ab and S genes (+/+/- pattern) by the TaqPath COVID-19 CE-IVD RT-PCR Kit manufactured by Thermofisher. By sequencing, a unique mutation, synonymous 28948C > T, was found in the N-negative B.1.177.75 strains. Although we do not have any knowledge upon the nucleotide sequences of the primers and probe adopted by this kit, it is likely that N gene dropout only occurs when 28948C > T is coupled with 28932C > T, this latter present, in turn, in all B.1.177.75 sequences available on public databases. Furthermore, epidemiological analysis was also performed. The majority of the N-negative B.1.177.75 cases belonged to two clusters apparently unrelated to each other and both clusters involved young people. However, the phylogeny for sequences containing the +/+/- pattern strongly supports a genetic connection and one common source for both clusters. Though, genetic comparison suggests a connection rather than indicating the independent emergence of the same mutation in two apparently unrelated clusters. This study highlights once more the importance of sharing genomic data to link apparently unrelated epidemiological clusters and to, remarkably, update molecular tests.

Retrospective Characterization of the 2006-2007 Swine Vesicular Disease Epidemic in Northern Italy by Whole Genome Sequence Analysis

Advances in the epidemiological tracing of pathogen transmission have been largely driven by the increasing characterisation of whole-genome sequence data obtained at a finer resolution from infectious disease outbreaks. Dynamic models that integrate genomic and epidemiological data further enhance inference on the evolutionary history and transmission dynamics of epidemic outbreaks by reconstructing the network of ‘who-infected-whom’. Swine Vesicular Disease (SVD) was present in Italy from 1966 until 2015, and since the mid-1990s, it has mainly been circulating within Italy’s central-southern regions with sporadic incursions to the north of the country. However, a recrudescence of SVD in northern Italy was recorded between November 2006 and October 2007, leading to a large-scale epidemic that significantly affected the intensive pig industry of the Lombardy region. In this study, by using whole-genome sequence data in combination with epidemiological information on disease occurrences, we report a retrospective epidemiological investigation of the 2006–2007 SVD epidemic, providing new insights into the transmission dynamics and evolutionary mode of the two phases that characterised the epidemic event. Our analyses support evidence of undetected premises likely missed in the chain of observed infections, of which the role as the link between the two phases is reinforced by the tempo of SVD virus evolution. These silent transmissions, likely resulting from the gradual loss of a clear SVD clinical manifestation linked to sub-clinical infections, may pose a risk of failure in the early detection of new cases. This study emphasises the power of joint inference schemes based on genomic and epidemiological data integration to inform the transmission dynamics of disease epidemics, ultimately aimed at better disease control.