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.
Highlights
Periodontal disease (PD) manifests as bacterial biofilms that lead to gum inflammation, recession, and, in later stages, degradation of the bone and tooth loss
We used non-metric multidimensional scaling (NMDS) ordination to determine the clustering of samples by condition for microbes, cytokines, and metabolites for the periodontal treatment (PT) and sodium hypochlorite (SHT) study datasets
The most distinct separation was seen in the metabolites for disease class in the SHT study, where most of the samples in disease class “A” clustered together and the samples in class “C” split into two groups (Figure 2H); a similar pattern was observed in the metabolites for pocket depth (Supplementary Figure 2)
Summary
Periodontal disease (PD) manifests as bacterial biofilms (plaque) that lead to gum inflammation, recession, and, in later stages, degradation of the bone and tooth loss. Prior to the development of nextgeneration sequencing (NGS) technologies, a cluster of three species deemed the “red complex,” consisting of Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythia, was found to be associated with the PD clinical factors gum pocket depth and bleeding (Socransky et al, 1998). NGS technologies have revealed greater diversity of the oral microbiome and a complex relationship between microbiome composition and periodontal disease states, including an association between increasing microbial diversity and pocket depth (Kroes et al, 1999; Paster et al, 2001; Faveri et al, 2008; Griffen et al, 2012). High inter-patient diversity of the oral microbiome complicates deciphering the relationship between periodontal treatments or changes in disease state on the associated microbiome (Kumar et al, 2006; Schwarzberg et al, 2014; Califf et al, 2017)
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