Searching sediment for signs of avian influenza
June 13, 2018
by MEAT+POULTRY Staff
VANCOUVER, B.C. – A partnership including the BC Ministry of Agriculture, BC Centre for Disease Control Public Health Laboratory and Univ. of British Columbia has produced a new approach to surveillance for avian influenza viruses that uses a genomics-based test to identify and characterize AI viruses in wetland sediments.
Led by Drs. Chelsea Himsworth, Jane Pritchard, William Hsiao, Natalie Prystajecky and Agatha Jassem the test successfully detected AI viruses in a significant portion of sediment samples compared to less than 1 percent in the current Canadian national wild bird AI surveillance program. Also, an outbreak virus was found in wetland throughout the Fraser Valley in southwestern British Columbia. Researchers believed this information could have been used to better-contain an outbreak of highly pathogenic AI that struck British Columbia in 2014 to 2015. That outbreak impacted 13 poultry farms and resulted in the loss of approximately 240,000 birds.
The research was funded in part by Genome British Columbia (Genome BC). A new phase of the research project will further evaluate the genomics-based test. The project is called “Genomic Analysis of Wetland Sediment as a Tool for Avian Influenza Surveillance and Prevention.” The research follows previous work and will focus on what steps are necessary to move the technology to implementation from successful proof-of-concept initiative.
Genome BC, the BC Ministry of Agriculture, the Canadian Food Inspection Agency (CFIA), Agriculture and Agri-Food Canada, Investment Agriculture Foundation of BC, and the Sustainable Poultry Farming Group combined to invest more than $2.5 million.
“This investment allows Dr. Himsworth and the team to refine and validate the AI sediment surveillance with genomics technologies, methodology and field approach,” Dr. Catalina Lopez-Correa, Chief Scientific Officer and vice president, Sectors, at Genome BC, said in a statement. “Most importantly it allows for the identification of the optimal combination of AI surveillance techniques for maximum efficiency and efficacy.”