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Deep sequencing of the oral microbiome reveals signatures of periodontal disease.

Liu B, Faller LL, Klitgord N, Mazumdar V, Ghodsi M, Sommer DD, Gibbons TR, Treangen TJ, Chang YC, Li S, Stine OC, Hasturk H, Kasif S, Segrè D, Pop M, Amar S - PLoS ONE (2012)

Bottom Line: While much is known about individual species associated with pathogenesis, the system-level mechanisms underlying the transition from health to disease are still poorly understood.Furthermore, diseased samples share a common structure that was not found in completely healthy samples, suggesting that the disease state may occupy a narrow region within the space of possible configurations of the oral microbiome.Despite a modest level of sequencing (~2 lanes Illumina 76 bp PE) and high human DNA contamination (up to ~90%) we were able to partially reconstruct several oral microbes and to preliminarily characterize some systems-level differences between the healthy and diseased oral microbiomes.

View Article: PubMed Central - PubMed

Affiliation: Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America.

ABSTRACT
The oral microbiome, the complex ecosystem of microbes inhabiting the human mouth, harbors several thousands of bacterial types. The proliferation of pathogenic bacteria within the mouth gives rise to periodontitis, an inflammatory disease known to also constitute a risk factor for cardiovascular disease. While much is known about individual species associated with pathogenesis, the system-level mechanisms underlying the transition from health to disease are still poorly understood. Through the sequencing of the 16S rRNA gene and of whole community DNA we provide a glimpse at the global genetic, metabolic, and ecological changes associated with periodontitis in 15 subgingival plaque samples, four from each of two periodontitis patients, and the remaining samples from three healthy individuals. We also demonstrate the power of whole-metagenome sequencing approaches in characterizing the genomes of key players in the oral microbiome, including an unculturable TM7 organism. We reveal the disease microbiome to be enriched in virulence factors, and adapted to a parasitic lifestyle that takes advantage of the disrupted host homeostasis. Furthermore, diseased samples share a common structure that was not found in completely healthy samples, suggesting that the disease state may occupy a narrow region within the space of possible configurations of the oral microbiome. Our pilot study demonstrates the power of high-throughput sequencing as a tool for understanding the role of the oral microbiome in periodontal disease. Despite a modest level of sequencing (~2 lanes Illumina 76 bp PE) and high human DNA contamination (up to ~90%) we were able to partially reconstruct several oral microbes and to preliminarily characterize some systems-level differences between the healthy and diseased oral microbiomes.

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Systems-level analysis reveals the disease state to occupy a narrow region within the space of possible states for the microbiome.A – Shannon diversity calculated from 16S rDNA data is significantly higher in diseased samples (community is more diverse). B – Principal Component Analysis of the taxonomic compositions from 16S rDNA (empty symbols) and pooled WGS data (filled symbols). Disease samples cluster together in the bottom left corner. Sample H31 (tooth with incipient periodontal disease from an otherwise healthy patient) clusters together with the disease samples. C – Principal component analysis of the enzyme content of samples based on metagenomic sequencing. The PCA graph shows a tighter clustering of disease samples (red) relative to the healthy ones (blue). This suggests that the disease state may be linked to a specific metabolic configuration, and that the space of disease configurations is more constrained than the healthy one. Replicates (forward/reverse reads from one or two instrument lanes) are shown separately as identical symbols, and exhibit minimal metabolic variation within each sample. D – Comparison of relative frequencies of tetramers (4 bp motifs) in metagenomic reads across disease cases (in red, P1 vs. P2) and across control cases (in blue, H1 vs. H2). Based on the relative frequencies of tetramers, disease samples are more similar to each other (points lie along the diagonal) than controls are to each other.
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pone-0037919-g003: Systems-level analysis reveals the disease state to occupy a narrow region within the space of possible states for the microbiome.A – Shannon diversity calculated from 16S rDNA data is significantly higher in diseased samples (community is more diverse). B – Principal Component Analysis of the taxonomic compositions from 16S rDNA (empty symbols) and pooled WGS data (filled symbols). Disease samples cluster together in the bottom left corner. Sample H31 (tooth with incipient periodontal disease from an otherwise healthy patient) clusters together with the disease samples. C – Principal component analysis of the enzyme content of samples based on metagenomic sequencing. The PCA graph shows a tighter clustering of disease samples (red) relative to the healthy ones (blue). This suggests that the disease state may be linked to a specific metabolic configuration, and that the space of disease configurations is more constrained than the healthy one. Replicates (forward/reverse reads from one or two instrument lanes) are shown separately as identical symbols, and exhibit minimal metabolic variation within each sample. D – Comparison of relative frequencies of tetramers (4 bp motifs) in metagenomic reads across disease cases (in red, P1 vs. P2) and across control cases (in blue, H1 vs. H2). Based on the relative frequencies of tetramers, disease samples are more similar to each other (points lie along the diagonal) than controls are to each other.

Mentions: The functional characterization reported above suggests that, beyond the taxonomic details, one can identify ecosystem-level signatures of periodontal disease consistent with its clinical manifestations. However, from the above analysis, it is still not clear whether these signatures reflect isolated instances of disease-related molecular processes, or fit into a coherent picture of the disease as a predictably different state of the whole oral microbial flora. We addressed this question by performing additional analyses at different levels of resolution, and found that a major systemic change seems to be identifiable between the healthy and diseased microbiomes. The diseased samples harbor a more diverse microbial community (as measured by the Shannon diversity index, Figure 3A), yet clustering analysis at the taxonomic level (Figure 1, Figure 3B) and in terms of enzyme content (Figure 3C), as well as pairwise comparisons of individual healthy and diseased samples based on tetramer (subsequences of length 4) frequencies (Figure 3D), all indicate that disease samples are more similar to each other than the healthy samples. In other words, the diseased state appears to be associated with a constrained and predictable region in the space of all possible states a microbiome can take. Thus, although the periodontal disease microbiomes are more diverse in terms of community structure, that structure is quite similar across different patients. In contrast, the healthy microbiome in any individual patient has relatively lower taxonomic diversity, but its exact composition differs significantly across patients.


Deep sequencing of the oral microbiome reveals signatures of periodontal disease.

Liu B, Faller LL, Klitgord N, Mazumdar V, Ghodsi M, Sommer DD, Gibbons TR, Treangen TJ, Chang YC, Li S, Stine OC, Hasturk H, Kasif S, Segrè D, Pop M, Amar S - PLoS ONE (2012)

Systems-level analysis reveals the disease state to occupy a narrow region within the space of possible states for the microbiome.A – Shannon diversity calculated from 16S rDNA data is significantly higher in diseased samples (community is more diverse). B – Principal Component Analysis of the taxonomic compositions from 16S rDNA (empty symbols) and pooled WGS data (filled symbols). Disease samples cluster together in the bottom left corner. Sample H31 (tooth with incipient periodontal disease from an otherwise healthy patient) clusters together with the disease samples. C – Principal component analysis of the enzyme content of samples based on metagenomic sequencing. The PCA graph shows a tighter clustering of disease samples (red) relative to the healthy ones (blue). This suggests that the disease state may be linked to a specific metabolic configuration, and that the space of disease configurations is more constrained than the healthy one. Replicates (forward/reverse reads from one or two instrument lanes) are shown separately as identical symbols, and exhibit minimal metabolic variation within each sample. D – Comparison of relative frequencies of tetramers (4 bp motifs) in metagenomic reads across disease cases (in red, P1 vs. P2) and across control cases (in blue, H1 vs. H2). Based on the relative frequencies of tetramers, disease samples are more similar to each other (points lie along the diagonal) than controls are to each other.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3366996&req=5

pone-0037919-g003: Systems-level analysis reveals the disease state to occupy a narrow region within the space of possible states for the microbiome.A – Shannon diversity calculated from 16S rDNA data is significantly higher in diseased samples (community is more diverse). B – Principal Component Analysis of the taxonomic compositions from 16S rDNA (empty symbols) and pooled WGS data (filled symbols). Disease samples cluster together in the bottom left corner. Sample H31 (tooth with incipient periodontal disease from an otherwise healthy patient) clusters together with the disease samples. C – Principal component analysis of the enzyme content of samples based on metagenomic sequencing. The PCA graph shows a tighter clustering of disease samples (red) relative to the healthy ones (blue). This suggests that the disease state may be linked to a specific metabolic configuration, and that the space of disease configurations is more constrained than the healthy one. Replicates (forward/reverse reads from one or two instrument lanes) are shown separately as identical symbols, and exhibit minimal metabolic variation within each sample. D – Comparison of relative frequencies of tetramers (4 bp motifs) in metagenomic reads across disease cases (in red, P1 vs. P2) and across control cases (in blue, H1 vs. H2). Based on the relative frequencies of tetramers, disease samples are more similar to each other (points lie along the diagonal) than controls are to each other.
Mentions: The functional characterization reported above suggests that, beyond the taxonomic details, one can identify ecosystem-level signatures of periodontal disease consistent with its clinical manifestations. However, from the above analysis, it is still not clear whether these signatures reflect isolated instances of disease-related molecular processes, or fit into a coherent picture of the disease as a predictably different state of the whole oral microbial flora. We addressed this question by performing additional analyses at different levels of resolution, and found that a major systemic change seems to be identifiable between the healthy and diseased microbiomes. The diseased samples harbor a more diverse microbial community (as measured by the Shannon diversity index, Figure 3A), yet clustering analysis at the taxonomic level (Figure 1, Figure 3B) and in terms of enzyme content (Figure 3C), as well as pairwise comparisons of individual healthy and diseased samples based on tetramer (subsequences of length 4) frequencies (Figure 3D), all indicate that disease samples are more similar to each other than the healthy samples. In other words, the diseased state appears to be associated with a constrained and predictable region in the space of all possible states a microbiome can take. Thus, although the periodontal disease microbiomes are more diverse in terms of community structure, that structure is quite similar across different patients. In contrast, the healthy microbiome in any individual patient has relatively lower taxonomic diversity, but its exact composition differs significantly across patients.

Bottom Line: While much is known about individual species associated with pathogenesis, the system-level mechanisms underlying the transition from health to disease are still poorly understood.Furthermore, diseased samples share a common structure that was not found in completely healthy samples, suggesting that the disease state may occupy a narrow region within the space of possible configurations of the oral microbiome.Despite a modest level of sequencing (~2 lanes Illumina 76 bp PE) and high human DNA contamination (up to ~90%) we were able to partially reconstruct several oral microbes and to preliminarily characterize some systems-level differences between the healthy and diseased oral microbiomes.

View Article: PubMed Central - PubMed

Affiliation: Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, United States of America.

ABSTRACT
The oral microbiome, the complex ecosystem of microbes inhabiting the human mouth, harbors several thousands of bacterial types. The proliferation of pathogenic bacteria within the mouth gives rise to periodontitis, an inflammatory disease known to also constitute a risk factor for cardiovascular disease. While much is known about individual species associated with pathogenesis, the system-level mechanisms underlying the transition from health to disease are still poorly understood. Through the sequencing of the 16S rRNA gene and of whole community DNA we provide a glimpse at the global genetic, metabolic, and ecological changes associated with periodontitis in 15 subgingival plaque samples, four from each of two periodontitis patients, and the remaining samples from three healthy individuals. We also demonstrate the power of whole-metagenome sequencing approaches in characterizing the genomes of key players in the oral microbiome, including an unculturable TM7 organism. We reveal the disease microbiome to be enriched in virulence factors, and adapted to a parasitic lifestyle that takes advantage of the disrupted host homeostasis. Furthermore, diseased samples share a common structure that was not found in completely healthy samples, suggesting that the disease state may occupy a narrow region within the space of possible configurations of the oral microbiome. Our pilot study demonstrates the power of high-throughput sequencing as a tool for understanding the role of the oral microbiome in periodontal disease. Despite a modest level of sequencing (~2 lanes Illumina 76 bp PE) and high human DNA contamination (up to ~90%) we were able to partially reconstruct several oral microbes and to preliminarily characterize some systems-level differences between the healthy and diseased oral microbiomes.

Show MeSH
Related in: MedlinePlus