Bovine Viral Diarrhoea Virus (BVDV) in Dairy Cattle: A Matched Case-Control Study

Comparative Analysis of the Prevalence of Bovine Viral Diarrhea Virus in Cattle Populations Based on Detection Methods: A Systematic Review and Meta-Analysis

Infectious diseases of cattle, including bovine viral diarrhea (BVD), pose a significant health threat to the global livestock industry. This study aimed to investigate the prevalence and risk factors associated with bovine viral diarrhea virus (BVDV) infections in cattle populations through a systematic review and meta-analysis. PubMed, Web of Science, and Scopus were systematically searched for relevant articles reporting the prevalence of and associated risk factors in studies published between 1 January 2000 and 3 February 2023. From a total of 5111 studies screened, 318 studies were included in the final analysis. BVDV prevalence in cattle populations was estimated using various detection methods. The analysis detected heterogeneity in prevalence, attributed to detection techniques and associated risk factors. Antibody detection methods exhibited a higher prevalence of 0.43, reflecting the cumulative effect of detecting both active and past infections. Antigen detection methods showed a prevalence of 0.05, which was lower than antibody methods. A prevalence of 0.08 was observed using nucleic acid detection methods. The health status of the examined cattle significantly influenced the prevalence of BVDV. Cattle with bovine respiratory disease complex (BRDC) exhibited higher antibody (prevalence of 0.67) and antigen (prevalence 0.23) levels compared to cattle with reproductive problems (prevalence 0.13) or diarrhea (prevalence 0.01). Nucleic acid detection methods demonstrated consistent rates across different health conditions. Age of cattle influenced prevalence, with higher rates in adults compared to calves. Risk factors related to breeding and reproduction, such as natural or extensive breeding and a history of abortion, were associated with increased prevalence. Coinfections with pathogens like bovine herpesvirus-1 or Neospora caninum were linked to higher BVDV prevalence. Management practices, such as commingling, introducing new cattle, and direct contact with neighboring farms, also influenced prevalence. Herd attributes, including larger herd size, and the presence of persistently infected cattle, were associated with higher prevalence. These findings indicated the importance of detection methods and risk factors in BVDV epidemiological studies.

Identification of key proteins of cytopathic biotype bovine viral diarrhoea virus involved in activating NF-κB pathway in BVDV-induced inflammatory response

Bovine viral diarrhoea virus (BVDV) is the etiologic agent of bovine viral diarrhea-mucosal disease, one of the most important viral diseases in cattle, with inflammatory diarrhea, enteritis, and mucosa necrosis as the major clinical manifestations. NF-κB is an important transcription complex that regulates the expression of genes involved in inflammation and immune responses. NLRP3 inflammasome plays a key role in the development of inflammatory diseases. However, whether the activation of NF-κB is crucial for BVDV infection-induced inflammatory responses remains unclear. The results of our present study showed that BVDV infection significantly activated the NF-κB pathway and promoted the expression of NLRP3 inflammasome components (NLRP3, ASC, pro-caspase 1) as well inflammatory cytokine pro-IL-1β in BVDV-infected bovine cells, resulting in the cleavage of pro-caspase 1 and pro-IL-1β into active form caspase 1 and IL-1β. However, the levels of the NLRP3 inflammasome components and inflammatory cytokines were obviously inhibited, as well the cleavage of pro-caspase 1 and pro-IL-1β in the pre-treated bovine cells with NF-κB-specific inhibitors after BVDV infection. Further, cytopathic biotype BVDV (cpBVDV) Erns and NS5A proteins with their key functional domains contributed to BVDV-induced inflammatory responses via activating the NF-κB pathway were confirmed experimentally. Especially, the NS5A can promote cholesterol synthesis and accelerate its augmentation, further activating the NF-κB signalling pathway. Conclusively, our data elucidate that the activation of NF-κB signaling pathway plays a crucial role in cpBVDV infection-induced inflammatory responses.

Quantification of risk factors for bovine viral diarrhea virus in cattle herds: A systematic search and meta-analysis of observational studies

Bovine viral diarrhea virus (BVDV) is endemic in many parts of the world, and multiple countries have implemented surveillance activities for disease control or eradication. In such control programs, the disease-free status can be compromised by factors that pose risks for introduction or persistence of the virus. The aim of the present study was to gain a comprehensive overview of possible risk factors for BVDV infection in cattle herds in Europe and to assess their importance. Papers that considered risk factors for BVDV infection in cattle were identified through a systematic search. Further selection of papers eligible for quantitative analysis was performed using a predefined checklist, including (1) appropriate region (i.e., studies performed in Europe), (2) representativeness of the study population, (3) quality of statistical analysis, and (4) availability of sufficient quantitative data. In total, 18 observational studies were selected. Data were analyzed by a random-effects meta-analysis to obtain pooled estimates of the odds of BVDV infection. Meta-analyses were performed on 6 risk factors: herd type, herd size, participation in shows or markets, introduction of cattle, grazing, and contact with other cattle herds on pasture. Significant higher odds were found for dairy herds (odds ratio, OR = 1.63, 95% confidence interval, CI: 1.06-2.50) compared with beef herds, for larger herds (OR = 1.04 for every 10 extra animals in the herd, 95% CI: 1.02-1.06), for herds that participate in shows or markets (OR = 1.45, 95% CI: 1.10-1.91), for herds that introduced cattle into the herd (OR = 1.41, 95% CI: 1.18-1.69), and for herds that share pasture or have direct contact with cattle of other herds at pasture (OR = 1.32, 95% CI: 1.07-1.63). These pooled values must be interpreted with care, as there was a high level of heterogeneity between studies. However, they do give an indication of the importance of the most frequently studied risk factors and can therefore assist in the development, evaluation, and optimization of BVDV control programs.

Development of a quantitative risk assessment of bovine viral diarrhea virus and bovine herpesvirus-1 introduction in dairy cattle herds to improve biosecurity

A quantitative risk assessment model was developed to estimate the annual probability of introducing bovine viral diarrhea virus (BVDV) and bovine herpesvirus 1 (BoHV-1) at the farm level through animal movements. Data from 2017 official animal movements, biosecurity questionnaires, scientific literature, and expert opinion from field veterinarians were taken into consideration for model input parameters. Purchasing or introducing cattle, rearing replacement heifers offsite, showing cattle at competitions, sharing transport vehicles with other herds, and transporting cattle in vehicles that have not been cleaned and disinfected were considered in the model. The annual probability of introducing BVDV or BoHV-1 through infected animals was very heterogeneous between farms. The median likelihoods of BVDV and BoHV-1introduction were 12 and 9%, respectively. Farms that purchased cattle from within their region (i.e., local movements) and shared transport with other farms had a higher probability for BVDV and BoHV-1 introduction. This model can be a useful tool to support decision-making on biosecurity measures that should be prioritized to reduce the probability of introduction of these 2 diseases in dairy herds.

What variables are important in predicting bovine viral diarrhea virus? A random forest approach

Bovine viral diarrhea virus (BVDV) causes one of the most economically important diseases in cattle, and the virus is found worldwide. A better understanding of the disease associated factors is a crucial step towards the definition of strategies for control and eradication. In this study we trained a random forest (RF) prediction model and performed variable importance analysis to identify factors associated with BVDV occurrence. In addition, we assessed the influence of features selection on RF performance and evaluated its predictive power relative to other popular classifiers and to logistic regression. We found that RF classification model resulted in an average error rate of 32.03% for the negative class (negative for BVDV) and 36.78% for the positive class (positive for BVDV).The RF model presented area under the ROC curve equal to 0.702. Variable importance analysis revealed that important predictors of BVDV occurrence were: a) who inseminates the animals, b) number of neighboring farms that have cattle and c) rectal palpation performed routinely. Our results suggest that the use of machine learning algorithms, especially RF, is a promising methodology for the analysis of cross-sectional studies, presenting a satisfactory predictive power and the ability to identify predictors that represent potential risk factors for BVDV investigation. We examined classical predictors and found some new and hard to control practices that may lead to the spread of this disease within and among farms, mainly regarding poor or neglected reproduction management, which should be considered for disease control and eradication.