Publications

A porcine reproductive and respiratory syndrome virus 2 strain was identified in lung samples from nursery piglets associated with a 17.15% mortality rate on a swine farm in Iowa, USA. Open reading frame 5 (ORF5) sequencing indicated that this strain is a restriction fragment length polymorphism (RFLP) 1-4-4 lineage 1C variant strain, and its complete coding genome sequence was determined.

This study evaluated the use of endemic enteric coronaviruses polymerase chain reaction (PCR)-negative testing results as an alternative approach to detect the emergence of animal health threats with similar clinical diseases presentation. This retrospective study, conducted in the United States, used PCR-negative testing results from porcine samples tested at six veterinary diagnostic laboratories. As a proof of concept, the database was first searched for transmissible gastroenteritis virus (TGEV) negative submissions between January 1 st , 2010, through April 29 th , 2013, when the first porcine epidemic diarrhea virus (PEDV) case was diagnosed. Secondly, TGEV- and PEDV-negative submissions were used to detect the porcine delta coronavirus (PDCoV) emergence in 2014. Lastly, encountered best detection algorithms were implemented to prospectively monitor the 2023 enteric coronavirus-negative submissions. Time series (weekly TGEV-negative counts) and Seasonal Autoregressive-Integrated Moving-Average (SARIMA) were used to control for outliers, trends, and seasonality. The SARIMA’s fitted and residuals were then subjected to anomaly detection algorithms (EARS, EWMA, CUSUM, Farrington) to identify alarms, defined as weeks of higher TGEV-negativity than what was predicted by models preceding the PEDV emergence. The best-performing detection algorithms had the lowest false alarms (number of alarms detected during the baseline) and highest time to detect (number of weeks between the first alarm and PEDV emergence). The best-performing detection algorithms were CUSUM, EWMA, and Farrington flexible using SARIMA fitted values, having a lower false alarm rate and identified alarms 4 to 17 weeks before PEDV and PDCoV emergences. No alarms were identified in the 2023 enteric negative testing results. The negative-based monitoring system functioned in the case of PEDV propagating epidemic and in the presence of a concurrent propagating epidemic with the PDCoV emergence. It demonstrated its applicability as an additional tool for diagnostic data monitoring of emergent pathogens having similar clinical disease as the monitored endemic pathogens.

) using the USDA Animal Disease Spread Model software (v3.5.10.0). In Phase 2, we calculated the probability of detecting ≥1 infected farm as a function of producer participation, farm-level sensitivity, farm-level prevalence, and sampling frequency. The participatory design was effective: ≥90% probability of detecting the notifiable pathogen at 0.05% farm prevalence (33 positive farms among 66,637 farms) when farm-level sensitivity was ≥20% and producer participation was ≥40%. Depending on the specimen collected, the shipment method, and the test selected, costs ranged from $0.03 to $0.07 USD (€0.02 to €0.06) per pig in inventory. Thus, a surveillance design based on collecting and testing specimens from a few targeted pigs on each of many farms would be both affordable and effective at a national level.

Disease management and epidemiological investigations of porcine reproductive and respiratory syndrome virus-type 2 (PRRSV-2) often rely on grouping together highly related sequences. In the USA, the last five years have seen a major shift within the swine industry when classifying PRRSV-2, beginning to move away from RFLP (restriction fragment length polymorphisms)-typing and adopting the use of phylogenetic lineage-based classification. However, lineages and sub-lineages are large and genetically diverse, making them insufficient for identifying new and emerging variants. Thus, within the lineage system, a dynamic fine-scale classification scheme is needed to provide better resolution on the relatedness of PRRSV-2 viruses to inform disease management and monitoring efforts and facilitate research and communication surrounding circulating PRRSV viruses. Here, we compare fine-scale systems for classifying PRRSV-2 variants (i.e., genetic clusters of closely related ORF5 sequences at finer scales than sub-lineage) using a database of 28,730 sequences from 2010 to 2021, representing >55% of the U.S. pig population. In total, we compared 140 approaches that differed in their tree-building method, criteria, and thresholds for defining variants within phylogenetic trees. Three approaches resulted in variant classifications that were reproducible and robust even when the input data or input phylogenies were changed. For these approaches, the average genetic distance among sequences belonging to the same variant was 2.1–2.5%, and the genetic divergence between variants was 2.5–2.7%. Machine learning classification algorithms were trained to assign new sequences to an existing variant with >95% accuracy, which shows that newly generated sequences can be assigned to a variant without repeating the phylogenetic and clustering analyses. Finally, we identified 73 sequence-clusters (dated <1 year apart with close phylogenetic relatedness) associated with circulation events on single farms. The percent of farm sequence-clusters with an ID change was 6.5–8.7% for our approaches. In contrast, ~43% of farm sequence-clusters had variation in their RFLP-type, further demonstrating how our proposed fine-scale classification system addresses shortcomings of RFLP-typing. Through identifying robust and reproducible classification approaches for PRRSV-2, this work lays the foundation for a fine-scale system that would more reliably group related field viruses and provide better resolution for decision-making surrounding disease management.

Developing and raising replacement heifers requires a large capital investment for producers. Therefore, it is imperative to discover traits and management practices to eliminate subfertile heifers prior to breeding and pregnancy determination. In this study, four years of data was analyzed from a centralized beef heifer development yard in the Midwest of the United States. The objective of this study was to analyze various heifer physical characteristics and management practices in order to quantify their impact on pregnancy and date of conception. Logistic regression models were built to investigate risk factors associated with conception to artificial insemination (AI), pregnancy by natural service after AI exposure, and pregnancy in the first 21-days of the breeding season. Age at entry, average daily gain from entry to breeding, pelvic width, and year were associated with AI pregnancy (p < 0.05). On the second model, average daily gain from entry to yearling weight, weight at breeding, weight at pregnancy diagnosis, and age at AI were significantly associated with pregnancy. There were no associations with reproductive tract score with any of the response variables analyzed. These results indicate there are physical measurements that can be used to improve the ability to select and develop heifers for improved reproductive performance.

Abstract Background A Tonsil-Oral-Scrubbing (TOSc) method was developed to sample the sow’s oropharyngeal and tonsillar area without snaring and has shown comparable porcine reproductive and respiratory syndrome virus (PRRSV) RNA detection rates with tonsil scraping in infected sows. This study investigated the effect of specific TOSc collection factors on the PRRSV RT-rtPCR results (detection rates and Ct values). Those factors include whether the sow was snared or not snared at TOSc collection (“snared” vs. “not snared”); whether the sow was laying down or standing at collection (“laying down” vs. “standing”); and type of collectors used for TOSc collection (“TOSc prototype” vs. “Spiral-headed AI catheter (SHAC)”). Volume of fluid was compared between “snared” and “not snared” groups, and collection time was compared between “laying down” and “standing” groups as well. Results The effect for each factor was assessed in three independent studies following the same design: TOSc was collected twice from each studied sow, once with the baseline level for a factor (“not snared”, or “standing”, or “TOSc prototype”), and another time followed by the other level of the paired factor (“snared”, “laying down”, or “SHAC”, correspondingly). Results showed that “not snared” TOSc had numerically higher PRRSV RNA detection rate (60.7% vs. 52.5%, p = 0.11), significantly lower median Ct values (31.9 vs. 32.3, p &lt; 0.01), and significantly higher volume of fluid than “snared” samples (1.8 mL vs. 1.2 mL, p &lt; 0.01); “laying down” TOSc samples did not differ statistically (60.7% vs. 60.7%) in the PRRSV RNA detection rate, obtained numerically lower median Ct values (30.9 vs. 31.3, p = 0.19), but took 40% less collection time compared to “standing” TOSc samples; samples collected using the “TOSc prototype” had numerically higher PRRSV RNA detection rate (91.7% vs. 88.3%, p = 0.27) and significantly lower median Ct values (32.8 vs. 34.5, p &lt; 0.01) than that from “SHAC”. Conclusions Under the conditions of this study best practices for TOSc collection aiming higher detection rate of PRRSV RNA while minimizing time for collection were suggested to be sampling TOSc without snaring, when sows are laying down, and using a prototype TOSc collector.

Family oral fluids (FOFs) are an aggregate sample type shown to be a cost-efficient and convenient option for determining the porcine reproductive and respiratory syndrome virus (PRRSV) status of weaning age pigs. This study investigated the effect of pooling PRRSV-positive FOF samples with known PRRSV-negative FOF samples at different levels (1:3, 1:5, 1:10, 1:20) on the probability of PRRSV RNA detection by reverse-transcriptase real-time polymerase chain reaction (RT-rtPCR). Mathematical models were built to assess how much the probability of RT-rtPCR detection changed with increasing PCR-positive samples present within pools and how partially sampling a farrowing room influenced the probability of PCR detection of PRRSV RNA in pooled samples at different prevalence scenarios. A general example of a guideline for FOF-based sampling under different prevalence scenarios to detect PRRSV RNA by RT-rtPCR with at least 95% certainty is presented.At the sample level, the probability of detecting PRRSV RNA by RT-rtPCR decreased from 100% to 87%, 68% and 26% when diluting up to 1:20 for PRRSV positive FOF having initial Cycle threshold (Ct) below 34, between 34 and 36, or above 36, respectively. When the prevalence among crates is near-zero (1 or 2 positive out of 56), the most cost-efficient farrowing room sampling strategy to detect PRRSV RNA with at least 95% certainty was pooling FOF samples up to 1:10; at higher prevalence (≥ 3 of 56 crates positive), the most cost-efficient strategy was submitting samples in pools of 20. Subsampling a farrowing room for FOF pools was also demonstrated to be a valuable cost saving strategy. Overall, based on the conditions of this study, pooling FOFs up to 1:20 is recommended in situations of cost constraint and regardless of pooling level chosen, capturing as many litters as possible improves herd sensitivity.

ABSTRACT: Computerized microtomography is the gold standard examination for the evaluation of the three-dimensional bone structure. This experiment was developed to evaluate the structure and bone quality of Caiman yacare with metabolic bone disease using high resolution computerized microtomography (μCT). The animals were distributed into four groups: G1 - hyperphosphatemic diet with sun exposure deprivation (n=4), G2 - hyperphosphatemic diet with sun exposure (n=4), G3 - balanced diet with sun exposure deprivation (n=4), and G4 - balanced diet with exposure to sunlight (n=4). The parameters for the trabecular bone (Trabecular Number, Trabecular Thickness, Trabecular Separation, Bone Pattern Factor, Fractal Dimension, Euler Number, Structural Model Index, Degree of Anisotropy, Eigenvalues 1, 2 and 3, and Centroides X, Y and Z), and cortical bone (Number of Closed Pores, Volume of Closed Pores, Surface of Closed Pores, Closed Porosity, Volume of Open Pores, Open Porosity and Total Porosity). The overall results showed that the structure and bone quality of group G3 and G4 were better than those of groups G1 and G2, and that the diet factor influenced more than the sun exposure factor. The computerized microtomography allowed to evaluate the quality of the cortical and trabecular bones of the Pantanal alligator tibia with osteometabolic disease. The diet and sun exposure factors influenced individually the results of the μCT parameters between the groups, demonstrating the functional and structural complexity. Thus, these parameters can contribute to the interpretation of the mechanical behavior of bones and correlate them with the risk of lesions and fractures associated with osteometabolic diseases.

Porcine reproductive and respiratory syndrome virus (PRRSV) is the most important swine pathogen affecting the United States of America (USA), leading to significant economic losses. Despite advances in diagnostic testing, there remains a gap in understanding the genetic evolution of PRRSV, especially in tracking the emergence of novel sequences and their spread across different regions and production stages. This research addresses this gap by developing a systematic methodology for directly collecting and analyzing PRRSV ORF5 sequences from veterinary diagnostic laboratories. The study aimed to identify trends among collected sequences and emerging PRRSV sequences by integrating nucleotide sequence data with metadata, providing critical insights into their geographic distribution, collected specimens, swine age groups, lineages, variants, and restriction fragment length polymorphism (RFLP) patterns. As of December 2024, the database housed 115,643 PRRSV ORF5 sequences. Sublineages 1B, 1A, 1H, and 1C.5 were the major wild-type PRRSV sequences detected over time, whereas vaccine-like strains comprised mostly of sublineages 5A and 8A. A novel sequence detection system was implemented, categorizing sequences based on similarity thresholds, ambiguities, and length criteria, identifying 167 novel sequences for the period between 2010 and 2024, whereas only three had continued detection in the field over time, forming clusters of detection. The analysis of these novel sequences highlighted significant trends, including the dominance of grow-finish animals in sequence origin and the high number of detections of sublineage 5A. Production sites located in states with the largest swine inventory have contributed to the most frequent detection of new PRRSV strains. Additionally, the development of a web-based tool provides end users with the capability to search sequences similar to their query sequence, providing macroepidemiological information and genetic sequence features to support PRRSV management and control. Real-time PRRSV sequencing data analysis informs producers and veterinarians of any upcoming novel sequences and trends of detection. The findings are intended to enhance current surveillance efforts and support more effective strategies for managing PRRSV outbreaks, ultimately safeguarding animal health, economic sustainability in the swine industry, and ultimately contributing to national food production sovereignty through pork-derived products.

The Pestivirus genus comprises species that affect animal health and productivity worldwide. Members of the Suidae family are hosts for classical swine fever virus (CSFV), an important pathogen tracked by the World Organization for Animal Health (OIE). However, swine are also susceptible to other pestivirus species that can result in disease or compromise CSFV detection. We searched for pestivirus infection in swine sera collected from 320 backyard pig herds in southern Brazil. We used reverse-transcription PCR primers for Bungowannah virus; atypical porcine pestivirus (APPV); and a panpestivirus pair that detects bovine viral diarrhea virus (BVDV)-1, -2, and HoBi-like pestivirus (HoBiPeV), border disease virus (BDV), and CSFV. Two samples were positive using the panpestivirus primer pair and were classified as BVDV-1d and -2a, respectively. Serum samples were tested for virus neutralization against BVDV-1a, -1b, and -2 strains, resulting in 28 (4.4%) positive samples. Of those, 16 samples had the highest titers against BVDV-1a (2), BVDV-1b (5), and BVDV-2 (9). Our results indicate that Bungowannah virus, APPV, CSFV, BDV, and HoBiPeV have not been circulating in these specific backyard swine populations. However, ruminant pestiviruses were detected and must be considered in future pestivirus control programs conducted in Brazil.