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. 2017 Feb 28;2(1):e00188-16.
doi: 10.1128/mSystems.00188-16. eCollection Jan-Feb 2017.

Community-Level Differences in the Microbiome of Healthy Wild Mallards and Those Infected by Influenza A Viruses

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Free PMC article

Community-Level Differences in the Microbiome of Healthy Wild Mallards and Those Infected by Influenza A Viruses

Holly H Ganz et al. mSystems. .
Free PMC article

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. IMPORTANCE Seasonal influenza causes 3 to 5 million severe illnesses and 250,000 to 500,000 human deaths each year. While pandemic influenza viruses emerge only periodically, they can be devastating-for example, the 1918 H1N1 pandemic virus killed more than 20 million people. IAVs infect the respiratory tract and cause significant morbidity and mortality in humans. In contrast, IAVs infect the gastrointestinal tract of waterfowl, producing little pathology. Recent studies indicated that viruses can alter the microbiome at the respiratory and gastrointestinal mucosa, but there are no reports of how the microbiota of the natural host of influenza is affected by infection. Here we find that the mallard microbiome is altered during IAV infection. Our results suggest that detailed examination of humans and animals infected with IAVs may reveal individualized microbiome profiles that correspond to health and disease. Moreover, future studies should explore whether the altered microbiome facilitates maintenance and transmission of IAVs in waterfowl populations.

Keywords: biomarkers; influenza; machine learning; mallard; microbiome; network modeling.

Figures

FIG 1
FIG 1
(a) IAV-positive (IAV+) and IAV mallards also differed in OTU richness, Shannon index, and OTU evenness results. (b) Proportions of bacterial phyla in the cloacal microbiome differed between IAV+ and IAV mallards.
FIG 2
FIG 2
(a) Networks of cooccurrence patterns for OTUs found in mallards that tested either positive for IAV (IAV+) or negative for IAV (IAV). Networks of D+ and D data were constructed from the IAV+ and IAV networks by adding edges that differed in edge weight by more than a threshold ε value of 0.2 from those of their D networks. Once D networks were created, we applied an edge threshold value of 0.5 to remove the edges with low edge weights. The clusters obtained for the D+ and D networks with the overlapping 41 are shown next to the difference networks and are colored (in D cluster). (b) Toy graph illustrating our method.
FIG 3
FIG 3
Log-scaled abundances of OTUs under different IAV conditions. (a) A total of 41 overlapping OTUs. (b) A total of 8 DIROM OTUs, not including the overlapping 41. (c) A total of 46 G-test OTUs, not including the overlapping 41. (d) A total of 7 OTUs that were uniquely found to be highly cooccurring in IAV+ networks. (e) A total of 80 OTUs that were uniquely found to be highly cooccurring in IAV networks. (f) A total of 20 OTUs that were mutually significant under both IAV conditions.
FIG 4
FIG 4
The number and abundance of the overlapping 41 OTUs in juvenile mallards.
FIG 5
FIG 5
(a) Venn diagram showing the degree of overlap of the following different approaches: G-test for significant differences between groups (with Bonferroni corrections for false-discovery rate), DIROM, and unique network clusters. Unique networks consist of the set of OTUs that were obtained by clustering in D+ or D but not in both. (b) Induced subnetwork of IAV+ and IAV for the overlapping 41 OTUs. Both subnetworks are shown as roughly complete graphs (i.e., there exists an edge within every pair of OTUs); however, the edge weights in IAV are all of high value (average weighted degree of 25.4) and thus form a robust cluster showing strong cooccurrence patterns among the nodes, whereas in IAV+, edge weights among 41 overlapping OTUs were on average very low (average weighted degree of 1.7).

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