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Compositional Data Analysis of Periodontal Disease Microbial Communities.

ABSTRACT: Periodontal disease (PD) is a chronic, progressive polymicrobial disease that induces a strong host immune response. Culture-independent methods, such as next-generation sequencing (NGS) of bacteria 16S amplicon and shotgun metagenomic libraries, have greatly expanded our understanding of PD biodiversity, identified novel PD microbial associations, and shown that PD biodiversity increases with pocket depth. NGS studies have also found PD communities to be highly host-specific in terms of both biodiversity and the response of microbial communities to periodontal treatment. As with most microbiome work, the majority of PD microbiome studies use standard data normalization procedures that do not account for the compositional nature of NGS microbiome data. Here, we apply recently developed compositional data analysis (CoDA) approaches and software tools to reanalyze multiomics (16S, metagenomics, and metabolomics) data generated from previously published periodontal disease studies. CoDA methods, such as centered log-ratio (clr) transformation, compensate for the compositional nature of these data, which can not only remove spurious correlations but also allows for the identification of novel associations between microbial features and disease conditions. We validated many of the studies' original findings, but also identified new features associated with periodontal disease, including the genera Schwartzia and Aerococcus and the cytokine C-reactive protein (CRP). Furthermore, our network analysis revealed a lower connectivity among taxa in deeper periodontal pockets, potentially indicative of a more "random" microbiome. Our findings illustrate the utility of CoDA techniques in multiomics compositional data analysis of the oral microbiome.

SUBMITTER: Sisk-Hackworth L

PROVIDER: S-EPMC8165185 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Periodontitis is a polymicrobial infectious disease that causes breakdown of the periodontal ligament and alveolar bone. We employed a meta-omics approach that included microbial 16S rRNA amplicon sequencing, shotgun metagenomics, and tandem mass spectrometry to analyze sub- and supragingival biofilms in adults with chronic periodontitis pre- and posttreatment with 0.25% sodium hypochlorite. Microbial samples were collected with periodontal curettes from 3- to 12-mm-deep periodontal pockets at the baseline and at 2 weeks and 3 months. All data types showed high interpersonal variability, and there was a significant correlation between phylogenetic diversity and pocket depth at the baseline and a strong correlation (rho = 0.21; P = 0.008) between metabolite diversity and maximum pocket depth (MPD). Analysis of subgingival baseline samples (16S rRNA and shotgun metagenomics) found positive correlations between abundances of particular bacterial genera and MPD, including Porphyromonas, Treponema, Tannerella, and Desulfovibrio species and unknown taxon SHD-231. At 2 weeks posttreatment, we observed an almost complete turnover in the bacterial genera (16S rRNA) and species (shotgun metagenomics) correlated with MPD. Among the metabolites detected, the medians of the 20 most abundant metabolites were significantly correlated with MPD pre- and posttreatment. Finally, tests of periodontal biofilm community instability found markedly higher taxonomic instability in patients who did not improve posttreatment than in patients who did improve (UniFrac distances; t = -3.59; P = 0.002). Interestingly, the opposite pattern occurred in the metabolic profiles (Bray-Curtis; t = 2.42; P = 0.02). Our results suggested that multi-omics approaches, and metabolomics analysis in particular, could enhance treatment prediction and reveal patients most likely to improve posttreatment. IMPORTANCE Periodontal disease affects the majority of adults worldwide and has been linked to numerous systemic diseases. Despite decades of research, the reasons for the substantial differences among periodontitis patients in disease incidence, progressivity, and response to treatment remain poorly understood. While deep sequencing of oral bacterial communities has greatly expanded our comprehension of the microbial diversity of periodontal disease and identified associations with healthy and disease states, predicting treatment outcomes remains elusive. Our results suggest that combining multiple omics approaches enhances the ability to differentiate among disease states and determine differential effects of treatment, particularly with the addition of metabolomic information. Furthermore, multi-omics analysis of biofilm community instability indicated that these approaches provide new tools for investigating the ecological dynamics underlying the progressive periodontal disease process.

Project description:The use of Next Generation Sequencing (NGS) techniques has generated a wide variety of blood microbiome data. Due to the large variation in bacterial DNA profiles between studies and the likely high concentrations of cell-free bacterial DNA in the blood, it is still not clear how such microbiome data relates to viable microbiota. For these reasons much remains to be understood about the true nature of any possible healthy blood microbiota and of bacteraemic events associated with disease. The gut, reproductive tracts, skin, and oral cavity are all likely sources of blood-borne bacteria. Oral bacteria, especially those associated with periodontal diseases, are also commonly associated with cardiovascular diseases such as infective endocarditis, and also have been linked to rheumatoid arthritis and Alzheimer's disease. Periodontal treatment, dental probing, and toothbrushing have been shown to cause transient bacteraemia and oral bacteria from the phyla Firmicutes (e.g. Streptococci) and Bacteroidetes (e.g. Porphyromonas) are found in cardiovascular lesions (CVD). Many studies of blood bacterial DNA content however, find Proteobacteria DNA to be the dominant microbiome component, suggesting a gut origin. Most studies of this type use total DNA extracted from either whole blood or blood fractions, such as buffy coat. Here, using a method that purifies DNA from intact bacterial cells only, we examined blood donated by those with active, severe periodontitis and periodontally healthy controls and show that 43-52% of bacterial species in blood are classified as oral. Firmicutes, consisting largely of members of the Streptococcus mitis group and Staphylococcus epidermidis, were predominant at 63.5% of all bacterial sequences detected in periodontal health and, little changed at 66.7% in periodontitis. Compared to studies using total DNA Proteobacteria were found here at relatively low levels in blood at 13.3% in periodontitis and 17.6% in health. This study reveals significant phylogenetic differences in blood bacterial population profiles when comparing periodontal health to periodontal disease cohorts.

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Compositional Data Analysis of Periodontal Disease Microbial Communities.

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