Genomic analysis of wetland sediment as a tool for avian influenza surveillance and prevention

Avian Influenza (AI) is a viral disease of poultry that has significant negative impacts on agriculture and can be zoonotic. Wild waterfowl are the natural reservoir of AI, shedding virus in the feces and spreading AI among... [ view full abstract ]

Avian Influenza (AI) is a viral disease of poultry that has significant negative impacts on agriculture and can be zoonotic. Wild waterfowl are the natural reservoir of AI, shedding virus in the feces and spreading AI among different geographic locations during their annual migrations. Current surveillance techniques focused on testing individual wild birds for the presence of AI have significant limitations. Indeed, these techniques, which were in place prior to the 2014/2015 H5N2 AI outbreak in British Columbia, Canada, failed to detect the presence of AI in wild birds in advance of domestic poultry cases. The objective of this project was to develop a new AI surveillance approach based on genomic analysis of wetland sediments. Given that waterfowl congregate on wetlands, by testing wetland sediments we may be able to efficiently screen a large number of waterfowl encompassing a wide range of potential reservoir species. During the 2014/2015 BC AI outbreak, sediment samples (n = 341) were collected from wetlands and small water bodies on infected farms. After RNA extraction, samples were analyzed by RT-qPCR for the AI matrix gene and by a novel targeted-resequencing technology. Among the 300 wetland samples, 23 (7.7%) were PCR positive and 49 (16.3%) were suspect-positive. Among the 41 on-farm samples, 15 (36.6%) were positive and 6 (14.6%) were suspect positive. Thus far 44 samples (21 PCR positive, 13 suspect positive, and 10 negative) have been analyzed by targeted-resequencing. AI was detected in 34 samples, including H5N2 clustering with the outbreak strain in 17 samples. Preliminary results suggest that this novel approach will be effective for AI surveillance in waterfowl, particularly given that detection rates among individual waterfowl included in the current Canadian National Surveillance Program are ~1%. Our goal is to use sediment surveillance as the cornerstone for a provincial AI early warning system.