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Assessing the Variation within the Oral Microbiome of Healthy Adults

Nearing, Jacob T., DeClercq, Vanessa, Van Limbergen, Johan, Langille, Morgan G. I.·2020·mSphere
DOI 10.1128/msphere.00451-20PMC PMC7529435

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Abstract

The human oral cavity is inhabited by a diverse community of microbes, known as the human oral microbiome. These microbes play a role in maintaining both oral and systemic health and, as such, have been proposed to be useful biomarkers of disease. However, to identify these biomarkers, we first need to determine the composition and variation of the healthy oral microbiome. In this report, we investigate the oral microbiome of 1,049 healthy individuals to determine which genera and amplicon sequence variants are commonly found between individual oral microbiomes. We then further investigate how lifestyle, anthropometric, and dietary choices impact overall microbiome composition. Interestingly, the results from this investigation showed that while many features were significantly associated with oral microbiome composition, no single biological factor explained a variation larger than 2%. These results indicate that future work on biomarker detection may be encouraged by the lack of strong confounding factors.

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FIG 1 Flowchart of sample selection from the Atlantic Partnership for Tomorrow’s Health cohort. A total of 35,577 individuals participated in the Atlantic Partnership for Tomorrow’s Health cohort, and ∼9,000 individuals provided saliva samples. Of those, a subset of 1,214 saliva samples from healthy individuals underwent 16S rRNA gene sequencing. Samples below 5,000 reads were filtered out, and two data sets were created for discovery and validation analysis.
FIG 1 Flowchart of sample selection from the Atlantic Partnership for Tomorrow’s Health cohort. A total of 35,577 individuals participated in the Atlantic Partnership for Tomorrow’s Health cohort, and ∼9,000 individuals provided saliva samples. Of those, a subset of 1,214 saliva samples from healthy individuals underwent 16S rRNA gene sequencing. Samples below 5,000 reads were filtered out, and two data sets were created for discovery and validation analysis.CC BY · PubMed Central
FIG 2 Atlantic Canadian oral microbiome composition is dominated by the genus Veillonella and is relatively similar at the genus level but highly variable at the ASV level. Samples were from the Atlantic Partnership for Tomorrow’s Health project (n = 1,049). Samples were subsampled to a depth of 5,000 reads. (A) Genera that had a mean relative abundance of less than 1% were grouped into “Other.” (B) Genera were removed at different sample presence cutoffs, and the remaining total mean relative abundance of nonfiltered genera was then calculated. (C) ASVs were removed at different sample presence cutoffs, and the remaining total mean relative abundance of nonfiltered ASVs was then calculated.
FIG 2 Atlantic Canadian oral microbiome composition is dominated by the genus Veillonella and is relatively similar at the genus level but highly variable at the ASV level. Samples were from the Atlantic Partnership for Tomorrow’s Health project (n = 1,049). Samples were subsampled to a depth of 5,000 reads. (A) Genera that had a mean relative abundance of less than 1% were grouped into “Other.” (B) Genera were removed at different sample presence cutoffs, and the remaining total mean relative abundance of nonfiltered genera was then calculated. (C) ASVs were removed at different sample presence cutoffs, and the remaining total mean relative abundance of nonfiltered ASVs was then calculated.CC BY · PubMed Central
FIG 3 Various anthropometric, dietary, and lifestyle features are significantly associated with oral microbiome composition. Saliva samples were from the Atlantic Partnership for Tomorrow’s Health cohort (n = 741). Samples were subsampled to a depth of 5,000 reads. Two different metrics measuring beta diversity were tested, weighted Unifrac distances (A) and Bray-Curtis dissimilarity (B), using a PERMANOVA test while controlling for differences in DNA extraction and correction for false discovery (q < 0.1). Relationships between significant features, samples, and genera that were present in at least 10% of samples were then visualized by redundancy analysis (RDA) on center-log-ratio genus count tables. (C) Genera are colored by phylum and labeled numerically.
FIG 3 Various anthropometric, dietary, and lifestyle features are significantly associated with oral microbiome composition. Saliva samples were from the Atlantic Partnership for Tomorrow’s Health cohort (n = 741). Samples were subsampled to a depth of 5,000 reads. Two different metrics measuring beta diversity were tested, weighted Unifrac distances (A) and Bray-Curtis dissimilarity (B), using a PERMANOVA test while controlling for differences in DNA extraction and correction for false discovery (q < 0.1). Relationships between significant features, samples, and genera that were present in at least 10% of samples were then visualized by redundancy analysis (RDA) on center-log-ratio genus count tables. (C) Genera are colored by phylum and labeled numerically.CC BY · PubMed Central
FIG 4 Differentially abundant genera and ASVs whose abundance profiles are associated with features found to influence oral microbiome composition. Genera (A) and ASVs (B) meeting a false discovery rate of q < 0.1 using the Corncob R package, which uses beta-binomial regressions. Each feature’s false discovery rate was corrected separately, and each was tested to control for differences in DNA extraction and differential variability within that feature. Ordinal variables were converted into a ranked scale for testing, and all features except for sex were scaled. The asterisk indicates that sex was treated as a categorical value; therefore, the magnitude is not directly comparable to other log odd ratios.
FIG 4 Differentially abundant genera and ASVs whose abundance profiles are associated with features found to influence oral microbiome composition. Genera (A) and ASVs (B) meeting a false discovery rate of q < 0.1 using the Corncob R package, which uses beta-binomial regressions. Each feature’s false discovery rate was corrected separately, and each was tested to control for differences in DNA extraction and differential variability within that feature. Ordinal variables were converted into a ranked scale for testing, and all features except for sex were scaled. The asterisk indicates that sex was treated as a categorical value; therefore, the magnitude is not directly comparable to other log odd ratios.CC BY · PubMed Central
FIG 5 Various predicted pathway abundances are associated with features significantly associated with overall microbiome composition. Pathway abundances were predicted from 16S rRNA gene sequencing data using PICRUSt2. Predicted pathway abundances meeting an FDR of <0.05 and an effect size of |0.05| log odds were considered significant associations using the Corncob R package. Each feature’s false discovery rate was corrected separately, and each was tested to control for differences in DNA extraction and differential variability within that feature. Ordinal variables were converted into a ranked scale for testing, and all features except for sex were scaled. The asterisk indicates that sex was treated as a categorical value; therefore, the magnitude is not directly comparable to other log odd ratios.
FIG 5 Various predicted pathway abundances are associated with features significantly associated with overall microbiome composition. Pathway abundances were predicted from 16S rRNA gene sequencing data using PICRUSt2. Predicted pathway abundances meeting an FDR of <0.05 and an effect size of |0.05| log odds were considered significant associations using the Corncob R package. Each feature’s false discovery rate was corrected separately, and each was tested to control for differences in DNA extraction and differential variability within that feature. Ordinal variables were converted into a ranked scale for testing, and all features except for sex were scaled. The asterisk indicates that sex was treated as a categorical value; therefore, the magnitude is not directly comparable to other log odd ratios.CC BY · PubMed Central

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Citations

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Metadata

Authors
Nearing, J. T., DeClercq, V., Van Limbergen, J., & Langille, M. G. I.
Affiliations
  • Department of Microbiology and Immunology, Dalhousie University, Halifax, Nova Scotia, Canada
  • Population Cancer Research Program, Dalhousie University, Halifax, Nova Scotia, Canada
  • Division of Pediatric Gastroenterology &amp; Nutrition, Emma Children’s Hospital, Amsterdam University Medical Centers, Amsterdam, The Netherlands
ORCID
Nearing, Jacob T.
Venue
mSphere
Publisher
American Society for Microbiology
Published
2020-10-28
Volume
5
Issue
5
ISSN
2379-5042
DOI
10.1128/msphere.00451-20
PMC
PMC7529435
Cited by
117
References
9
Language
en
Licence
Funders
  • Canadian Foundation of Innovation John R. Evans Leadership fund
  • Nova Scotia Health Research Foundation
  • Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (2016-05039)
  • Gouvernement du Canada | Canadian Institutes of Health Research

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