Clinical Diagnostic Sensitivity and Specificity of Metagenomic Sequencing and qPCR for Detection of Viruses Associated with Bovine Respiratory Disease Estimated using Bayesian Latent Class Models

Emmanuel Donbraye¹Lianne McLeod¹Zhijian Chai¹Stacey R. Lacoste¹E. Luke McCarthy²Janet E. Hill³Nathan E.N. Erickson¹Matthew G. Links²Simon J.G. Otto^4,5Yanyun Huang^6,7Cheryl L. Waldner^1*

• ¹Department of Large Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Canada
• ²Department of Animal and Poultry Science, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, Canada
• ³Department of Veterinary Microbiology, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Canada
• ⁴HEAT-AMR (Human-Environment-Animal Transdisciplinary AMR) Research Group, School of Public Health, University of Alberta, Edmonton, Canada
• ⁵Centre for Healthy Communities, School of Public Health, University of Alberta, Edmonton, Canada
• ⁶Prairie Diagnostic Services Inc, Saskatoon, Canada
• ⁷Department of Veterinary Pathology, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Canada

Very few studies have examined diagnostic sensitivity and specificity of available laboratory tests for bovine respiratory pathogens, and fewer have examined test performance from animals before clinical disease. In this study, Bayesian latent class modelling (BLCM) was used to assess test performance without a gold standard on nasal swabs collected from 19 western Canadian feedlots. Viruses associated with bovine respiratory disease (BRD) were identified using qPCR from a commercial laboratory from 760 nasal swabs collected from fall-placed calves (FPC) and yearlings (YRL) at and shortly after feedlot arrival. Using BLCM, qPCR results were compared to nanopore metagenomic sequencing. Based on BLCM, sensitivities and specificities were estimated for detection of bovine coronavirus (BCoV), bovine herpesvirus type 1 (BoHV-1), bovine parainfluenza virus type 3 (BPIV-3), bovine respiratory syncytial virus (BRSV), and influenza D virus (IDV). Estimates were not available for bovine viral diarrheal virus (BVDV) because qPCR did not detect this virus. Diagnostic sensitivity of qPCR was higher than metagenomic sequencing for BCoV (qPCR 0.90, 95% CrI 0.81-0.99; sequencing 0.35, 95% CrI 0.25-0.46) and BoHV-1 (qPCR 0.39, 95% CrI 0.19-0.99; sequencing 0.04, 95% CrI 0.01-0.15). However, diagnostic sensitivity of metagenomic sequencing was higher than qPCR for BRSV (qPCR 0.32, 95% CrI 0.22-0.43; sequencing 0.60, 95% CrI 0.44-0.77). Clinical sensitivity was not significantly different for BPIV-3 (qPCR 0.42, 95% CrI 0.21-0.66; sequencing 0.52, 95% CrI 0.19-0.87) or IDV (qPCR 0.65, 95% CrI 0.53-0.79; sequencing 0.60, 95% CrI 0.48-0.73). Diagnostic specificity was comparable for most viruses, except for BCoV, where metagenomic sequencing (BCoV 0.91, 95% CrI 0.88-0.95) outperformed qPCR (BCoV 0.59, 95% CrI 0.51-0.68). The specificity and sensitivity for BRD-associated bacteria from the same metagenomic data were also similar to those for culture and qPCR. Sensitivities of both nanopore metagenomic sequencing and qPCR were moderate to very low for most viruses. While tests varied in detecting individual viruses, this study suggest nanopore metagenomic sequencing offers a potential alternative for diagnostic laboratories to identify three of six important BRD viruses as well as bacteria associated with BRD.