This year the Science in Practice award, given by the University of Minnesota’s College of Veterinary Medicine, will be presented to Dr. Bob Thompson, DVM with PIC (Pig Improvement Company).
“Boehringer Ingelheim is pleased to join the University of Minnesota in congratulating Dr. Thompson, an outstanding swine practitioner whose work ensures pig health is a priority from start to finish,” says Lara Sheeley, director of marketing for the BI swine division. “BI is committed to supporting swine industry leaders in their pursuit of knowledge and science to drive innovative solutions.”
The Allen D. Leman Science in Practice Award is given each year at the Allen D. Leman Swine Conference to a swine practitioner who has shown an exceptional ability to utilize science in day to day practice. The swine practitioner who receives the award bears the primary responsibility to move research into the field. Nominations and selection of the winner are governed by the Swine Faculty Planning Committee at the College of Veterinary Medicine, University of Minnesota.
“For more than 20 years this award has honored some of the most accomplished veterinary leaders in the swine industry,” says Bob Morrison, DVM, MBA, Ph.D., professor in the Department of Veterinary Population Medicine at the University of Minnesota. “We are excited to partner with Boehringer Ingelheim this year in honoring Dr. Thompson.”
The Science in Practice award reception will take place during the Allen D. Leman Swine Conference, September 16−19, 2017 at the Saint Paul RiverCentre. Visit www.lemanswine.umn.edu for more information and to register for the conference.
The swine group was well represented at the 2017 MVMA meeting. Ms. Alyssa Anderson and Dr. Karine Ludwig Takeuti both gave a presentation on diagnostic tools to detect Mycoplasma hyopneumoniae infections. Drs. Jorge Garrido Mantilla and Fabian Chamba Pardo shared the latest update on swine influenza. Dr. Amy Kinsley explained how the structure of a swine farm can influence disease persistence. Dr. Talita Resende shared the advantages of a fairly recent diagnostic technique: in situ hybridization. Congratulations to Ms. Alyssa Anderson who also received a $5,000 Food Animal Scholarship from the MVM Foundation!
Today, we are very pleased to report that a new indirect ELISA to identify Senecavirus A antibodies has been validated at the University of Minnesota and is now available for our Veterinary Diagnostic Laboratory clients. This ELISA targets specifically antibodies against Viral Protein 2 (VP2) and has a sensitivity of 94.2% and a specificity of 89.7%. The test does not cross react with antibodies against Foot-and-Mouth Disease allowing for a quick differentiation between a Senecavirus A outbreak and a costly foreign animal disease.
Background: Senecavirus A (SVA), a member of the family Picornaviridae, genus Senecavirus, is a recently identified single-stranded RNA virus closely related to members of the Cardiovirus genus. SVA was originally identified as a cell culture contaminant and was not associated with disease until 2007 when it was first observed in pigs with Idiopathic Vesicular Disease (IVD). Vesicular disease is sporadically observed in swine, is not debilitating, but is significant due to its resemblance to foreign animal diseases, such as foot-and-mouth disease (FMD), whose presence would be economically devastating to the United States. IVD disrupts swine production until foreign animal diseases can be ruled out. Identification and characterization of SVA as a cause of IVD will help to quickly rule out infection by foreign animal diseases.
Methods: We have developed and characterized an indirect ELISA assay to specifically identify serum antibodies to SVA. Viral protein 1, 2 and 3 (VP1, VP2, VP3) were expressed, isolated, and purified from E. coli and used to coat plates for an indirect ELISA. Sera from pigs with and without IVD symptoms as well as a time course following animals from an infected farm, were analyzed to determine the antibody responses to VP1, VP2, and VP3.
Results: Antibody responses to VP2 were higher than VP1 and VP3 and showed high affinity binding on an avidity ELISA. ROC analysis of the SVA VP2 ELISA showed a sensitivity of 94.2% and a specificity of 89.7%. Compared to IFA, the quantitative ELISA showed an 89% agreement in negative samples and positive samples from 4–60 days after appearance of clinical signs. Immune sera positive for FMDV, encephalomyocarditis virus, and porcine epidemic diarrhea virus antibodies did not cross-react.
Conclusions: A simple ELISA based on detection of antibodies to SVA VP2 will help to differentially diagnose IVD due to SVA and rule out the presence of economically devastating foreign animal diseases.
An article published in the Journal of Veterinary Diagnostic Investigation (JVDI) presents a competitive Enzyme-Linked ImmunoSorbent Assay (cELISA) and a virus neutralization test (VNT), both validated for the screening of Senecavirus A in a research setting, by the National Centre for Foreign Animal disease (NCFAD). The diagnostic specificity and sensitivity were 98.2% and 96.9% for the cELISA, and 99.6% (99.0–99.9%) and 98.2% (95.8–99.4%) for the VNT, respectively.
In Canada and the USA alike, Senecavirus A is a challenge for producers and veterinarians because of its clinical similarity to Food and Mouth Disease (FMD). Indeed, Senecavirus A, is a causative agent of swine vesicular disease with lesions developing on the snout, around the mouth and on the coronary band of the feet. Therefore, being able to differentiate Senecavirus A infections from FMD rapidly is of utmost importance to be able to take the appropriate measures.
The University of Minnesota, Veterinary Diagnostic Laboratory has developed an ImmunoFluorescence Assay (IFA) to detect antibodies against Senecavirus A. This test was used as a reference for the validation of the cELISA and VNT established by Drs. Goolia, Yang, Babiuk, and Nfon from NCFAD in collaboration with Drs. Vannucci and Patnayak from the UMN-VDL.
Abstract: Senecavirus A (SVA; family Picornaviridae) is a nonenveloped, single-stranded RNA virus associated with idiopathic vesicular disease (IVD) in swine. SVA was detected in pigs with IVD in Brazil, United States, Canada, and China in 2015, triggering the need to develop and/or validate serologic assays for SVA. Our objective was to fully validate a previously developed competitive enzyme-linked immunosorbent assay (cELISA) as a screening test for antibodies to SVA. Additional objectives included the development and validation of a virus neutralization test (VNT) as a confirmatory test for SVA antibody detection, and the comparison of the cELISA, VNT, and an existing immunofluorescent antibody test (IFAT) for the detection of SVA antibodies in serial bleeds from SVA outbreaks. The diagnostic specificity and sensitivity were 98.2% (97.2–98.9%) and 96.9% (94.5–98.4%) for the cELISA, and 99.6% (99.0–99.9%) and 98.2% (95.8–99.4%) for the VNT, respectively. There was strong agreement among cELISA, VNT, and IFAT when compared based on kappa coefficient. Based on these performance characteristics, these tests are considered suitable for serologic detection of SVA in pigs.
A collaborative work between the University of Minnesota, UC- Davis, and Pipestone Veterinary Services was published this past month in the journal Frontiers in Veterinary Science.
Between-farm animal movement, despite being an essential factor of infectious disease spread is not currently recorded in the US. The objective of this project was to create a model to predict animal movement based on between-site distance, ownership, and production type of the sending and receiving farms. The model was able to predict animal movement in the south-central region of the study area with a high aggregation. It also showed an overlap with the distribution of outbreaks of porcine reproductive and respiratory syndrome (PRRS) in this area.
Abstract: Between-farm animal movement is one of the most important factors influencing the spread of infectious diseases in food animals, including in the US swine industry. Understanding the structural network of contacts in a food animal industry is prerequisite to planning for efficient production strategies and for effective disease control measures. Unfortunately, data regarding between-farm animal movements in the US are not systematically collected and thus, such information is often unavailable. In this paper, we develop a procedure to replicate the structure of a network, making use of partial data available, and subsequently use the model developed to predict animal movements among sites in 34 Minnesota counties. First, we summarized two networks of swine producing facilities in Minnesota, then we used a machine learning technique referred to as random forest, an ensemble of independent classification trees, to estimate the probability of pig movements between farms and/or markets sites located in two counties in Minnesota. The model was calibrated and tested by comparing predicted data and observed data in those two counties for which data were available. Finally, the model was used to predict animal movements in sites located across 34 Minnesota counties. Variables that were important in predicting pig movements included between-site distance, ownership, and production type of the sending and receiving farms and/or markets. Using a weighted-kernel approach to describe spatial variation in the centrality measures of the predicted network, we showed that the south-central region of the study area exhibited high aggregation of predicted pig movements. Our results show an overlap with the distribution of outbreaks of porcine reproductive and respiratory syndrome, which is believed to be transmitted, at least in part, though animal movements. While the correspondence of movements and disease is not a causal test, it suggests that the predicted network may approximate actual movements. Accordingly, the predictions provided here might help to design and implement control strategies in the region. Additionally, the methodology here may be used to estimate contact networks for other livestock systems when only incomplete information regarding animal movements is available.