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doi: 10.1371/journal.pone.0056111. Epub 2013 Feb 6.

A multi-omic systems-based approach reveals metabolic markers of bacterial vaginosis and insight into the disease

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A multi-omic systems-based approach reveals metabolic markers of bacterial vaginosis and insight into the disease

Carl J Yeoman et al. PLoS One.2013.

Abstract

Background: Bacterial vaginosis (BV) is the most common vaginal disorder of reproductive-age women. Yet the cause of BV has not been established. To uncover key determinants of BV, we employed a multi-omic, systems-biology approach, including both deep 16S rRNA gene-based sequencing and metabolomics of lavage samples from 36 women. These women varied demographically, behaviorally, and in terms of health status and symptoms.

Principal findings: 16S rRNA gene-based community composition profiles reflected Nugent scores, but not Amsel criteria. In contrast, metabolomic profiles were markedly more concordant with Amsel criteria. Metabolomic profiles revealed two distinct symptomatic BV types (SBVI and SBVII) with similar characteristics that indicated disruption of epithelial integrity, but each type was correlated to the presence of different microbial taxa and metabolites, as well as to different host behaviors. The characteristic odor associated with BV was linked to increases in putrescine and cadaverine, which were both linked to Dialister spp. Additional correlations were seen with the presence of discharge, 2-methyl-2-hydroxybutanoic acid, and Mobiluncus spp., and with pain, diethylene glycol and Gardnerella spp.

Conclusions: The results not only provide useful diagnostic biomarkers, but also may ultimately provide much needed insight into the determinants of BV.

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Conflict of interest statement

Competing Interests:Corresponding author B. White is a PLOS ONE Editorial Board member. This does not alter the authors' adherence to all of the PLOS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. Richness and Diversity of Each Sample.
Rarefaction curves showing the richness of microbiomes for all samples colored by Nugent score (A; green = 0–3, orange = 4–6, red = 7–10) or Amsel classification (B; red = positive, green = negative) are presented along with Shannon diversity indexes (C), with samples grouped by Nugent score or Amsel criteria and colored as in rarefaction curves.
Figure 2
Figure 2. Heatmap of Taxonomic Enrichment by Sample.
Shown is a heatmap of the relative enrichment of the most abundant thirty microbial genera across the entire sample set and the relationships among samples. Highly abundant genera tend toward bright green, while less abundant genera tend toward blue, as shown in the key. Dendrograms show the relationship among samples. Red bars in the dendrogram show the relationship of samples with symptomatic BV. Nugent scores are presented beneath the dendrogram.
Figure 3
Figure 3. Nonmetric Multidimensional Scaling Analyses.
Shown are nMDS plots of the 16S rRNA reads clustered by genus for all taxa (A), or for justGardnerella andLactobacillus (B), or metabolite profiles (C). Samples from patients determined by Amsel criteria to have BV are shown in red, while samples determined by Amsel to be healthy are shown in green. Samples with a high (7–10; up-pointing triangles), moderate (4–6; diamonds) and low (0–3; down-pointing triangles) are indicated. The two metabotypes are delineated by hollow (type I) and solid (type II) symbols.
Figure 4
Figure 4. Heatmap of Metabolite Enrichment by Sample.
Shown is a heatmap of the relative enrichment of each metabolite across the entire sample set and the relationships among samples and among metabolites. Highly abundant metabolites tend toward red, while less abundant metabolites tend toward blue. Dendrograms show the relationship among samples (top) and among metabolites (left). The two BV metabotypes observed (I and II) are labeled at their branching point. Red bars in the top dendrogram show the relationship of samples with symptomatic BV. Nugent scores are shown for each sample. Metabolite mentioned in-text are labeled, others are numbered as in Table S2.
Figure 5
Figure 5. Venn Diagram of Discriminative Metabolites.
Shown is a Venn diagram of the numbers of unique and shared metabolites that delineate high Nugent scoring (≥7), Type I symptomatic BV or Type II symptomatic BV from other samples.
Figure 6
Figure 6. SBVI Sub-Network View of Linear Relationships among Variables.
Pearson’s (between parametric data) and Spearman's (between non-parametric and either parametric or non-parametric data) correlations >0.6 (green) or<−0.4 (red) are shown as edges connecting patient metadata relating to demographics, hygiene and sexual behaviors and sexual practices, OTUs, microbial genera, metabolites and patient symptoms. Figures presented represent sub-networks of the complete network (Fig S2). Node identities are listed or described in the key for figure 7. The identities of numbered metabolites are listed in Table S2.
Figure 7
Figure 7. SBVII Sub-Network View of Linear Relationships among Variables.
Pearson's (between parametric data) and Spearman's (between non-parametric and either parametric or non-parametric data) correlations >0.6 (green) or<−0.4 (red) are shown as edges connecting patient metadata relating to demographics, hygiene and sexual behaviors and sexual practices, OTUs, microbial genera, metabolites and patient symptoms. Figures presented represent sub-networks of the complete network (Fig S2). Node identities are listed or described in the key. The identities of numbered metabolites are listed in Table S2.
Figure 8
Figure 8. Cell Membrane Degradation.
Shown is a diagram depicting classical components of a cell membrane that is depleted in either of the metabolome profiles of the two symptomatic BV types.
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