Abstract
Waterfowl, especially ducks and geese, are primary reservoirs for influenza A viruses (IAVs) that evolve and emerge as important pathogens in domestic animals and humans. In contrast to humans, where IAVs infect the respiratory tract and cause significant morbidity and mortality, IAVs infect the gastrointestinal tract of waterfowl and cause little or no pathology and are spread by fecal-oral transmission. For this reason, we examined whether IAV infection is associated with differences in the cloacal microbiome of mallards (Anas platyrhyncos), an important host of IAVs in North America and Eurasia. We characterized bacterial community composition by sequencing the V4 region of 16S rRNA genes. IAV-positive mallards had lower species diversity, richness, and evenness than IAV-negative mallards. Operational taxonomic unit (OTU) cooccurrence patterns were also distinct depending on infection status. Network analysis showed that IAV-positive mallards had fewer significant cooccurring OTUs and exhibited fewer coassociation patterns among those OTUs than IAV-negative mallards. These results suggest that healthy mallards have a more robust and complex cloacal microbiome. By combining analytical approaches, we identified 41 bacterial OTUs, primarily representatives of Streptococcus spp., Veillonella dispar, and Rothia mucilaginosa, contributing to the observed differences. This study found that IAV-infected wild mallards exhibited strong differences in microbiome composition relative to noninfected mallards and identified a concise set of putative biomarker OTUs. Using Random Forest, a supervised machine learning method, we verified that these 41 bacterial OTUs are highly predictive of infection status.
| Original language | English (US) |
|---|---|
| Article number | e00188 |
| Journal | mSystems |
| Volume | 2 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2017 |
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Keywords
- Biomarkers
- Influenza
- Machine learning
- Mallard
- Microbiome
- Network modeling
ASJC Scopus subject areas
- Molecular Biology
- Physiology
- Genetics
- Biochemistry
- Modeling and Simulation
- Computer Science Applications
- Ecology, Evolution, Behavior and Systematics
- Microbiology
Cite this
Community-level differences in the microbiome of healthy wild mallards and those infected by influenza A viruses. / Ganz, Holly H.; Doroud, Ladan; Firl, Alana J.; Hird, Sarah M.; Eisen, Jonathan A; Boyce, Walter M.
In: mSystems, Vol. 2, No. 1, e00188, 01.01.2017.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Community-level differences in the microbiome of healthy wild mallards and those infected by influenza A viruses
AU - Ganz, Holly H.
AU - Doroud, Ladan
AU - Firl, Alana J.
AU - Hird, Sarah M.
AU - Eisen, Jonathan A
AU - Boyce, Walter M
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Waterfowl, especially ducks and geese, are primary reservoirs for influenza A viruses (IAVs) that evolve and emerge as important pathogens in domestic animals and humans. In contrast to humans, where IAVs infect the respiratory tract and cause significant morbidity and mortality, IAVs infect the gastrointestinal tract of waterfowl and cause little or no pathology and are spread by fecal-oral transmission. For this reason, we examined whether IAV infection is associated with differences in the cloacal microbiome of mallards (Anas platyrhyncos), an important host of IAVs in North America and Eurasia. We characterized bacterial community composition by sequencing the V4 region of 16S rRNA genes. IAV-positive mallards had lower species diversity, richness, and evenness than IAV-negative mallards. Operational taxonomic unit (OTU) cooccurrence patterns were also distinct depending on infection status. Network analysis showed that IAV-positive mallards had fewer significant cooccurring OTUs and exhibited fewer coassociation patterns among those OTUs than IAV-negative mallards. These results suggest that healthy mallards have a more robust and complex cloacal microbiome. By combining analytical approaches, we identified 41 bacterial OTUs, primarily representatives of Streptococcus spp., Veillonella dispar, and Rothia mucilaginosa, contributing to the observed differences. This study found that IAV-infected wild mallards exhibited strong differences in microbiome composition relative to noninfected mallards and identified a concise set of putative biomarker OTUs. Using Random Forest, a supervised machine learning method, we verified that these 41 bacterial OTUs are highly predictive of infection status.
AB - Waterfowl, especially ducks and geese, are primary reservoirs for influenza A viruses (IAVs) that evolve and emerge as important pathogens in domestic animals and humans. In contrast to humans, where IAVs infect the respiratory tract and cause significant morbidity and mortality, IAVs infect the gastrointestinal tract of waterfowl and cause little or no pathology and are spread by fecal-oral transmission. For this reason, we examined whether IAV infection is associated with differences in the cloacal microbiome of mallards (Anas platyrhyncos), an important host of IAVs in North America and Eurasia. We characterized bacterial community composition by sequencing the V4 region of 16S rRNA genes. IAV-positive mallards had lower species diversity, richness, and evenness than IAV-negative mallards. Operational taxonomic unit (OTU) cooccurrence patterns were also distinct depending on infection status. Network analysis showed that IAV-positive mallards had fewer significant cooccurring OTUs and exhibited fewer coassociation patterns among those OTUs than IAV-negative mallards. These results suggest that healthy mallards have a more robust and complex cloacal microbiome. By combining analytical approaches, we identified 41 bacterial OTUs, primarily representatives of Streptococcus spp., Veillonella dispar, and Rothia mucilaginosa, contributing to the observed differences. This study found that IAV-infected wild mallards exhibited strong differences in microbiome composition relative to noninfected mallards and identified a concise set of putative biomarker OTUs. Using Random Forest, a supervised machine learning method, we verified that these 41 bacterial OTUs are highly predictive of infection status.
KW - Biomarkers
KW - Influenza
KW - Machine learning
KW - Mallard
KW - Microbiome
KW - Network modeling
UR - http://www.scopus.com/inward/record.url?scp=85041549957&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85041549957&partnerID=8YFLogxK
U2 - 10.1128/mSystems.00188-16
DO - 10.1128/mSystems.00188-16
M3 - Article
AN - SCOPUS:85041549957
VL - 2
JO - mSystems
JF - mSystems
SN - 2379-5077
IS - 1
M1 - e00188
ER -