Frequency of Bovine Respiratory Disease Complex Bacterial and Viral Agents Using Multiplex Real-Time qPCR in Quebec, Canada, from 2019 to 2023

The bovine respiratory disease complex (BRD) is a multifactorial disease caused by various bacterial and viral pathogens. Using rapid pathogen detection techniques is helpful for tailoring therapeutic and preventive strategies in affected animals and herds. The objective of this study was to report the frequency of 10 pathogens by multiplex RT-qPCR on samples submitted for BRD diagnosis to a diagnostic laboratory (Biovet Inc., QC, Canada) in the Province of Quebec, Eastern Canada. From the 1st of January 2019 to the 31st of December 2023, a total of 1875 samples were analyzed. Most samples collected were individual samples (1547 of 1860 samples for which information was available (83.17%)), and the rest were from pooled samples of 2 (8.55%, <i>n</i> = 159) or ≥3 specimens (8.28%, <i>n</i> = 154). In 19.3% of the samples (<i>n</i> = 362), no pathogen was found, whereas 54.1% of samples had two or more different pathogens. Among the viruses, bovine coronavirus (BCV) was the most commonly found (27.5% of samples, <i>n</i> = 516), followed by bovine respiratory syncytial virus (BRSV) (17.7%, <i>n</i> = 332), whereas, for bacteria, <i>Pasteurella multocida</i> (50.1%, <i>n</i> = 940) and <i>Mannheimia haemolytica</i> (26.9%, <i>n</i> = 505) were the most common. The frequency of samples positive for <i>Histophilus somni</i>, <i>Mycoplasmopsis bovis</i>, influenza type D virus (IDV), bovine parainfluenza virus type 3 (BPI3V), bovine herpesvirus type 1 (BHV1), and bovine viral diarrhea virus (BVDV) was 22.6%, 22.4%, 4.6%, 4.3%, 2.7%, and 0.9%, respectively. In the multivariable Poisson regression model, the total number of pathogens increased with the number of animals in the pool, with an incidence risk ratio (IRR) of 1.15 (95% CI 1.02-1.29) and 1.32 (1.18-1.47) for 2 individuals in the pool and ≥3 individuals vs. individual samples, respectively. An increased number of pathogens were isolated in the winter season (IRR = 1.28 (95% CI 1.17-1.40)) compared to fall, and a lower number of pathogens were isolated in the summer compared to fall (IRR = 0.82 (95% CI 0.73-0.92)). These seasonal differences were mostly driven by the number of viruses isolated. This study gives interesting insights on the circulation of BRD pathogens in cattle from Eastern Canada.

Authors

Institutions

Publication Details

Journal
Veterinary Sciences
Published
2024-12-07
DOI
https://doi.org/10.3390/vetsci11120631
Citations
8
Primary Topic
Microbial infections and disease research
Type
article
Field-Weighted Citation Impact
2.76
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Frequency of Bovine Respiratory Disease Complex Bacterial and Viral Agents Using Multiplex Real-Time qPCR in Quebec, Canada, from 2019 to 2023

Sébastien Buczinski, Christian Savard, André Broes
8 citations
Veterinary Sciences
Microbial infections and disease research
2.76
article

Frequency of Bovine Respiratory Disease Complex Bacterial and Viral Agents Using Multiplex Real-Time qPCR in Quebec, Canada, from 2019 to 2023

Sébastien Buczinski, Christian Savard, André Broes
article en
8 citations

Abstract

The bovine respiratory disease complex (BRD) is a multifactorial disease caused by various bacterial and viral pathogens. Using rapid pathogen detection techniques is helpful for tailoring therapeutic and preventive strategies in affected animals and herds. The objective of this study was to report the frequency of 10 pathogens by multiplex RT-qPCR on samples submitted for BRD diagnosis to a diagnostic laboratory (Biovet Inc., QC, Canada) in the Province of Quebec, Eastern Canada. From the 1st of January 2019 to the 31st of December 2023, a total of 1875 samples were analyzed. Most samples collected were individual samples (1547 of 1860 samples for which information was available (83.17%)), and the rest were from pooled samples of 2 (8.55%, <i>n</i> = 159) or ≥3 specimens (8.28%, <i>n</i> = 154). In 19.3% of the samples (<i>n</i> = 362), no pathogen was found, whereas 54.1% of samples had two or more different pathogens. Among the viruses, bovine coronavirus (BCV) was the most commonly found (27.5% of samples, <i>n</i> = 516), followed by bovine respiratory syncytial virus (BRSV) (17.7%, <i>n</i> = 332), whereas, for bacteria, <i>Pasteurella multocida</i> (50.1%, <i>n</i> = 940) and <i>Mannheimia haemolytica</i> (26.9%, <i>n</i> = 505) were the most common. The frequency of samples positive for <i>Histophilus somni</i>, <i>Mycoplasmopsis bovis</i>, influenza type D virus (IDV), bovine parainfluenza virus type 3 (BPI3V), bovine herpesvirus type 1 (BHV1), and bovine viral diarrhea virus (BVDV) was 22.6%, 22.4%, 4.6%, 4.3%, 2.7%, and 0.9%, respectively. In the multivariable Poisson regression model, the total number of pathogens increased with the number of animals in the pool, with an incidence risk ratio (IRR) of 1.15 (95% CI 1.02-1.29) and 1.32 (1.18-1.47) for 2 individuals in the pool and ≥3 individuals vs. individual samples, respectively. An increased number of pathogens were isolated in the winter season (IRR = 1.28 (95% CI 1.17-1.40)) compared to fall, and a lower number of pathogens were isolated in the summer compared to fall (IRR = 0.82 (95% CI 0.73-0.92)). These seasonal differences were mostly driven by the number of viruses isolated. This study gives interesting insights on the circulation of BRD pathogens in cattle from Eastern Canada.

Veterinary SciencesVol. 11(12)
Université de Montréal (CA)
Good health and well-being
Openalex Percentile: Top 9%
Microbial infections and disease research
2.76
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.