Fatal Interstitial Pneumonia Associated with Bovine Coronavirus in Cows from Southern Italy

Expanding the range of the respiratory infectome in Australian feedlot cattle with and without respiratory disease using metatranscriptomics

BACKGROUND

Bovine respiratory disease (BRD) is one of the most common diseases in intensively managed cattle, often resulting in high morbidity and mortality. Although several pathogens have been isolated and extensively studied, the complete infectome of the respiratory complex consists of a more extensive range unrecognised species. Here, we used total RNA sequencing (i.e., metatranscriptomics) of nasal and nasopharyngeal swabs collected from animals with and without BRD from two cattle feedlots in Australia.

RESULTS

A high abundance of bovine nidovirus, influenza D, bovine rhinitis A and bovine coronavirus was found in the samples. Additionally, we obtained the complete or near-complete genome of bovine rhinitis B, enterovirus E1, bovine viral diarrhea virus (sub-genotypes 1a and 1c) and bovine respiratory syncytial virus, and partial sequences of other viruses. A new species of paramyxovirus was also identified. Overall, the most abundant RNA virus, was the bovine nidovirus. Characterisation of bacterial species from the transcriptome revealed a high abundance and diversity of Mollicutes in BRD cases and unaffected control animals. Of the non-Mollicutes species, Histophilus somni was detected, whereas there was a low abundance of Mannheimia haemolytica.

CONCLUSION

This study highlights the use of untargeted sequencing approaches to study the unrecognised range of microorganisms present in healthy or diseased animals and the need to study previously uncultured viral species that may have an important role in cattle respiratory disease. Video Abstract.

Development of a multiplexed Luminex assay for simultaneous detection of enteric viruses in cattle

Viral and bacterial gastroenteritis and diarrhea have long been a problem in livestock with devastating effects on animal health and production causing a heavy financial burden on producers. Therefore, the bead-based multiplex detection assay was created for simultaneous detection of three livestock viral diarrheic agents viz. bovine rotavirus (BRV), bovine coronavirus (BCoV) and bluetongue virus (BTV). The primers and probes for triplex MAGPIX assay for simultaneous detection of three enteric viruses were designed and the assay was optimized for hybridization temperature, primer-probe and bead concentrations. The newly developed MAGPIX assay was used to determine the prevalence of these diarrhea-associated viruses by testing 200 fecal samples collected from Haryana state of India during 2018-2019. The limit of detection of the developed triplex assay was 1 × 105, 1 × 104, and 1 × 105 RNA copies for BRV, BCoV, and BTV, respectively, being lower than the reverse transcription-quantitative polymerase chain reaction (RT-qPCR). However, it was higher than the conventional RT-PCR, showing it to be more sensitive. The newly developed MAGPIX assay was a rapid, cost-effective and high throughput diagnostic tool for identification of three major entero-pathogenic diarrhea associated viruses, either alone or in tandem, with the aim to prevent and control viral diarrhea in animals.

A First Assessment of SARS-CoV-2 Circulation in Bats of Central-Southern Italy

One serious concern associated with the SARS-CoV-2 pandemic is that the virus might spill back from humans to wildlife, which would render some animal species reservoirs of the human virus. We assessed the potential circulation of SARS-CoV-2 caused by reverse infection from humans to bats, by performing bat surveillance from different sites in Central-Southern Italy. We restricted our survey to sampling techniques that are minimally invasive and can therefore be broadly applied by non-medical operators such as bat workers. We collected 240 droppings or saliva from 129 bats and tested them using specific and general primers for SARS-CoV-2 and coronaviruses, respectively. All samples (127 nasal swabs and 113 faecal droppings) were negative for SARS-CoV-2, and these results were confirmed by testing the samples with the Droplet Digital PCR. Additionally, pancoronavirus end-point RT-PCR was performed, and no sample showed specific bands. This outcome is a first step towards a better understanding of the reverse transmission of this virus to bats. Although the occurrence of a reverse zoonotic pattern can only be fully established by serological testing, the latter might represent an in-depth follow-up to a broad-scale preliminary assessment performed with our approach. We encourage the systematic surveillance of bats to help prevent reverse zoonotic episodes that would jeopardize human health, as well as biodiversity conservation and management.

Decision tree analysis for pathogen identification based on circumstantial factors in outbreaks of bovine respiratory disease in calves

Respiratory tract infections continue to be a leading cause of economic loss, hampered animal welfare and intensive antimicrobial use in cattle operations, worldwide. To better target antimicrobial therapy, control and prevention towards the involved pathogens, there is a growing interest in microbiological tests on respiratory samples. However, these tests are time consuming, cost money and sampling might compromise animal welfare. Therefore, the objective of the present study was to develop immediately applicable decision trees for pathogen identification in outbreaks of bovine respiratory disease based on circumstantial factors. Data from a cross sectional study, involving 201 outbreaks of bovine respiratory disease in dairy and beef farms between 2016 and 2019 was used. Pathogens were identified by a semi-quantitative PCR (polymerase chain reaction) on a pooled non-endoscopic broncho-alveolar lavage sample from clinically affected animals. Potential risk factors of involved animals, environment, management and housing were obtained by enquiry. Classification and regression tree analysis was used for decision tree development with cross-validation. Different trees were constructed, involving a general 3-group classification tree (viruses, Mycoplasma bovis or Pasteurellaceae family) and a tree for each single pathogen. The general 3- group classification tree was 52.7 % accurate and had a sensitivity of 81.5 % and a specificity 52.2 % for viruses, respectively 51.7 % and 84.4 % for M. bovis and 28.9 % and 93.6 % for Pasteurellaceae. The single-pathogen trees were more specific than sensitive: Histophilus somni (Se = 25.8 %; Sp = 94.5 %), Mannheimia haemolytica (Se = 69.2 %; Sp = 70.6 %), bovine coronavirus (Se = 42.2 %; Sp = 89.6 %) and bovine respiratory syncytial virus (Se = 34.0 %; Sp = 96.6 %). For Pasteurella multocida, M. bovis and parainfluenzavirus type 3 no meaningful tree was obtained. The concept and trees are promising, but currently lack sensitivity and specificity in order to be a reliable tool for practice. For now, the obtained trees can already be informative for decision making to some extend depending on the end node in which an outbreak falls.

Overview of Control Programs for EU Non-regulated Cattle Diseases in Italy

The cattle industry is a major driving force for the Italian agricultural sector totalling about 5. 6 million heads for dairy and meat production together. It is particularly developed in the northern part of the country, where 70% of the whole Italian cattle population is reared. The cattle industry development in the rest of the country is hampered by the hard orography of the territories and a variety of socioeconomic features leading to the persistence of the traditional rural farming systems. The differences in the farming systems (industrial vs. traditional) also affect the health status of the farms. Whereas, Enzootic Bovine Leukosis (EBL) is almost eradicated across the whole country, in Southern Italy where Bovine Tuberculosis and Brucellosis are still present and Bluetongue is endemic due to the presence of the competent vector (Culicoides imicola), less investments are aimed at controlling diseases with economic impact or at improving farm biosecurity. On the other hand, with the eradication of these diseases in most part of the country, the need has emerged for reducing the economic burden of non-regulated endemic disease and control programs (CPs) for specific diseases have been implemented at regional level, based on the needs of each territory (for instance common grazing or trading with neighboring countries). This explains the coexistence of different types of programs in force throughout the country. Nowadays in Italy, among cattle diseases with little or no EU regulations only three are regulated by a national CP: Enzootic Bovine Leukosis, Bluetongue and Paratuberculosis, while Bovine Genital Campylobacteriosis and Trichomonosis are nationwide controlled only in breeding bulls. For some of the remaining diseases (Infectious Bovine Rhinotracheitis, Bovine Viral Diarrhea, Streptococcus agalactiae) specific CPs have been implemented by the regional Authorities, but for most of them a CP does not exist at all. However, there is a growing awareness among farmers and public health authorities that animal diseases have a major impact not only on the farm profitability but also on animal welfare and on the use of antibiotics in livestock. It is probable that in the near future other CPs will be implemented.

Bovine respiratory coronavirus enhances bacterial adherence by upregulating expression of cellular receptors on bovine respiratory epithelial cells

Bovine coronavirus (BCoV) is one of the agents causing bovine respiratory disease complex (BRDC), with single infection tending to be mild to moderate; the probability of developing pneumonia in BRDC may be affected by viral and bacterial combinations. Previously, we reported that bovine respiratory syncytial virus (BRSV) infection enhances adherence of Pasteurella multocida (PM) to cells derived from the bovine lower respiratory tract but that BRSV infection in cells derived from the upper respiratory tract reduces PM adherence. In this study, we sought to clarify whether the modulation of bacterial adherence to cells derived from the bovine upper and lower respiratory tract is shared by other BRDC-related viruses by infecting bovine epithelial cells from the trachea, bronchus and lung with BCoV and/or PM. The results showed that cells derived from both the upper and lower respiratory tract were susceptible to BCoV infection. Furthermore, all cells infected with BCoV exhibited increased PM adherence via upregulation of two major bacterial adhesion molecules, intercellular adhesion molecule-1 (ICAM-1) and platelet-activating factor receptor (PAF-R), suggesting that compared with BRSV infection, BCoV infection differentially modulates bacterial adherence. In summary, we identified distinct interaction between bovine respiratory viruses and bacterial infections.

Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning

In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the National Genomics Data Center repository, separating the genome of different virus strains from the Coronavirus family with 98.73% accuracy. The network's behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from the National Center for Biotechnology Information and Global Initiative on Sharing All Influenza Data repositories, and are proven to be able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n = 6 previously tested positive), delivering a sensitivity similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both automatically identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.