Targeted genomic sequencing of avian influenza viruses in wetland sediment from wild bird habitats
Bibliographic record
Abstract
Diverse influenza A viruses (IAVs) circulate in wild birds, including highly pathogenic strains that infect poultry and humans. Consequently, surveillance of IAVs in wild birds is a cornerstone of agricultural biosecurity and pandemic preparedness. Surveillance is traditionally done by testing wild birds directly, but obtaining these specimens is labor intensive, detection rates can be low, and sampling is often biased toward certain avian species. As a result, local incursions of dangerous IAVs are rarely detected before outbreaks begin. Testing environmental specimens from wild bird habitats has been proposed as an alternative surveillance strategy. These specimens are thought to contain diverse IAVs deposited by a broad range of avian hosts, including species that are not typically sampled by surveillance programs. To enable this surveillance strategy, we developed a targeted genomic sequencing method for characterizing IAVs in these challenging environmental specimens. It combines custom hybridization probes, unique molecular index-based library construction, and purpose-built bioinformatic tools, allowing IAV genomic material to be enriched and analyzed with single-fragment resolution. We demonstrated our method on 90 sediment specimens from wetlands around Vancouver, Canada. We recovered 2,312 IAV genome fragments originating from all eight IAV genome segments. Eleven hemagglutinin subtypes and nine neuraminidase subtypes were detected, including H5, the current global surveillance priority. Our results demonstrate that targeted genomic sequencing of environmental specimens from wild bird habitats could become a valuable complement to avian influenza surveillance programs.IMPORTANCEIn this study, we developed genome sequencing tools for characterizing avian influenza viruses in sediment from wild bird habitats. These tools enable an environment-based approach to avian influenza surveillance. This could improve early detection of dangerous strains in local wild birds, allowing poultry producers to better protect their flocks and prevent human exposures to potential pandemic threats. Furthermore, we purposefully developed these methods to contend with viral genomic material that is diluted, fragmented, incomplete, and derived from multiple strains and hosts. These challenges are common to many environmental specimens, making these methods broadly applicable for genomic pathogen surveillance in diverse contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".