Duck Slurry as a Matrix for Avian Influenza Virus Surveillance and Genetic Characterization in Domestic Flocks
Clément Castille, Laura Lebouteiller, Mathilda Walch, Guillaume CrovilleiD, Jean‐Luc GuériniD, Sébastien SoubièsiD
Abstract
Avian influenza viruses (AIVs) circulate widely in domestic and wild birds and continue to pose major risks to animal and public health, in particular those of high pathogenicity (HP). Ducks play a key role in AIV ecology due to frequent subclinical infections and high viral shedding. Efficient environmental surveillance approaches are needed to complement costly individual testing, especially in the context of large-scale anti-H5 vaccination as implemented in France. We evaluated duck slurry, the wastewater produced in duck fattening units, as a matrix for AIV detection and optimized molecular protocols for sensitive molecular detection and genetic characterization. Slurry samples (n = 172) were collected from 37 duck farms in southwestern France between May 2024 and March 2025. Among several extraction strategies, RNA extraction from the solid fraction using TRIzol LS followed by magnetic bead purification yielded the highest sensitivity. Using this optimized protocol, 82% of samples tested positive for AIV RNA, with 30% of positives being H5-positive; no H7 viruses were detected. Whole-segment RT-PCR and sequencing were successful for shorter genomic segments, enabling subtype determination, although virus isolation consistently failed. These findings demonstrate that slurry is a promising matrix for AIV detection, providing valuable molecular data. Slurry-based surveillance may therefore serve as an effective complement to individual testing and improve early warning capacities for influenza surveillance in a One Health perspective. ImportanceIn France, current surveillance of high pathogenicity avian influenza in ducks relies mainly on individual swabs, which may miss subclinical or transient infections, particularly with low-pathogenicity strains. By targeting slurry, a wastewater generated in duck fattening units, we developed an affordable and practical approach for environmental surveillance. Slurry samples captured high levels of avian influenza RNA and allowed partial genomic characterization despite failed virus isolation. This strategy is directly relevant to monitoring avian influenza viruses dynamics under current anti-H5 vaccination and can be extended to other livestock systems. Duck slurry-based surveillance thus represents an efficient One Health tool to strengthen preparedness against avian influenza outbreaks.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 0
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Targeted genomic sequencing of avian influenza viruses in wetlands sediment from wild bird habitats
Kevin S. Kuchinski, Michelle Coombe, … Natalie Prystajecky·Applied and Environmental MicrobiologyPeer-reviewed·Jan 23, 2024
- Evaluating sampling strategies for the detection of avian influenza viruses in the environment
Allison K. Miller, Lia Heremia, … Jemma L. Geoghegan·VirologyPeer-reviewed·Sep 2, 2026
- Avian influenza virus circulation and immunity in a wild urban duck population prior to and during a highly pathogenic H5N1 outbreak
Jordan Wight, Ishraq Rahman, … Andrew S. Lang·Veterinary ResearchPeer-reviewed·Nov 22, 2024
- Establishment of an avian influenza surveillance program in Australia's largest river basin
Micha V. Jackson, Heather M. McGinness, … Rebecca C. Doble·bioRxiv·Oct 2, 2026
- Zoonotic and Avian Pathogen Detections in Fecal and Sediment Samples - A Low-risk, High-throughput One Health Approach to Surveillance
Gabriela J. Rzeszutek, Jordan Wight, … Hannah L. Wallace·bioRxiv·May 6, 2026
- The role of ducks in detecting Highly Pathogenic Avian Influenza in small-scale backyard poultry farms
Steven Xingyu Wu, Christopher Neil Davis, … Michael J. Tildesley·PLoS Computational BiologyPeer-reviewed·Jan 9, 2026