Stability of healthy subgingival microbiome across space and time.
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ABSTRACT:
SUBMITTER: Tamashiro R
PROVIDER: S-EPMC8671439 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAlthough localized aggressive periodontitis (LAP), generalized aggressive periodontitis (GAP), and chronic periodontitis (CP) are microbially driven diseases, our inability to separate disease-specific associations from those common to all three forms of periodontitis has hampered biomarker discovery. Therefore, we aimed to map the genomic content of, and the biological pathways encoded by, the microbiomes associated with these clinical phenotypes. We also estimated the extent to which these biomes are governed by the Anna Karenina principle (AKP), which states that eubiotic communities are similar between individuals while disease-associated communities are highly individualized.MethodsWe collected subgingival plaque from 25 periodontally healthy individuals and diseased sites of 59 subjects with stage 3 periodontitis and used shotgun metagenomics to characterize the aggregate of bacterial genes.ResultsBeta-dispersion metrics demonstrated that AKP was most evident in CP, followed by GAP and LAP. We discovered broad dysbiotic signatures spanning the three phenotypes, with over-representation of pathways that facilitate life in an oxygen-poor, protein- and heme-rich, pro-oxidant environment and enhance capacity for attachment and biofilm formation. Phenotype-specific indicators were more readily evident in LAP microbiome than GAP or CP. Genes that enable acetate-scavenging lifestyle, utilization of alternative nutritional sources, oxidative and nitrosative stress responses, and siderophore production were unique to LAP. An attenuation of virulence-related functionalities and stress response from LAP to GAP to CP was apparent. We also discovered that clinical phenotypes of disease resolved variance in the microbiome with greater clarity than the newly established grading system. Importantly, we observed that one third of the metagenome of LAP is unique to this phenotype while GAP shares significant functional and taxonomic features with both LAP and CP, suggesting either attenuation of an aggressive disease or an early-onset chronic disease.ConclusionWithin the limitations of a small sample size and a cross-sectional study design, the distinctive features of the microbiomes associated with LAP and CP strongly persuade us that these are discrete disease entities, while calling into question whether GAP is a separate disease, or an artifact induced by cross-sectional study designs. Further studies on phenotype-specific microbial genes are warranted to explicate their role in disease etiology. Video Abstract.
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Project description:The oral microbiome plays a key role for caries, periodontitis, and systemic diseases. A method for rapid, high-resolution, robust taxonomic profiling of subgingival bacterial communities for early detection of periodontitis biomarkers would therefore be a useful tool for individualized medicine. Here, we used Illumina sequencing of the V1-V2 and V5-V6 hypervariable regions of the 16S rRNA gene. A sample stratification pipeline was developed in a pilot study of 19 individuals, 9 of whom had been diagnosed with chronic periodontitis. Five hundred twenty-three operational taxonomic units (OTUs) were obtained from the V1-V2 region and 432 from the V5-V6 region. Key periodontal pathogens like Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythia could be identified at the species level with both primer sets. Principal coordinate analysis identified two outliers that were consistently independent of the hypervariable region and method of DNA extraction used. The linear discriminant analysis (LDA) effect size algorithm (LEfSe) identified 80 OTU-level biomarkers of periodontitis and 17 of health. Health- and periodontitis-related clusters of OTUs were identified using a connectivity analysis, and the results confirmed previous studies with several thousands of samples. A machine learning algorithm was developed which was trained on all but one sample and then predicted the diagnosis of the left-out sample (jackknife method). Using a combination of the 10 best biomarkers, 15 of 17 samples were correctly diagnosed. Training the algorithm on time-resolved community profiles might provide a highly sensitive tool to detect the onset of periodontitis.
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Project description:OBJECTIVE:To describe the subgingival microbiome of individuals with Down syndrome (DS). METHODS:We conducted a cross-sectional observational study that obtained bacterial DNA samples from 50 patients with DS, 25 with periodontitis (PDS) and 25 with a healthy periodontal condition (HDS). The samples were analyzed by sequencing the 16S rRNA gene V3-V4 hypervariable region using the MiSeq System. Taxonomic affiliations were assigned using the naïve Bayesian classifier integrated in QIIME2 plugins. We evaluated the difference in bacteria abundance between the sample groups using Wilcoxon and Kruskal-Wallis tests. We evaluated the alpha diversity of the identified species using the Observed, Chao1metric, ACE and Shannon indices and evaluated beta diversity with principal coordinate analysis (registration code: 2018/510). RESULTS:Twenty-one genera and 39 bacterial species showed a significantly different abundance between the study groups. Among the genera, Porphyromonas, Treponema, Tannerella and Aggregatibacter were more abundant in the PDS group than in the HDS group, as were the less commonly studied Filifactor, Fretibacterium and Desulfobulbus genera. Among the species, Porphyromonas spp. and Tannerella spp. were the most abundant in the PDS group; the most abundant species in the HDS group were Pseudomonas spp., Granulicatella spp. and Gemella spp. CONCLUSION:Well-recognized periodontal pathogens and newly proposed pathogenic taxa were associated with periodontitis in patients with DS.
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Project description:Understanding of the oral microbiome in relation to periodontal disease in older adults is limited. The composition and diversity of the subgingival microflora and their oligotypes in health and levels of periodontal disease were investigated in this study on older postmenopausal women. The 16S rRNA gene was sequenced using the Illumina MiSeq platform in 1,206 women aged 53 to 81 y. Presence and severity of periodontal disease were defined by Centers for Disease Control and Prevention/American Academy of Periodontology criteria. Composition of the microbiome was determined by 16S rRNA amplicon sequencing and the abundance of taxa described by the centered log2-ratio (CLR) transformed operational taxonomic unit (OTU) values. Differences according to periodontal disease status were determined by analysis of variance with Bonferroni correction. Bacteria oligotypes associated with periodontal disease and health were determined by minimum entropy decomposition and their functions estimated in silico using PICRUSt. Prevalence of none/mild, moderate, and severe periodontal disease was 25.1%, 58.3%, and 16.6%, respectively. Alpha diversity of the microbiome differed significantly across the 3 periodontal disease categories. β-Diversity differed between no/mild and severe periodontal disease, although considerable overlap was noted. Of the 267 bacterial species identified at ≥0.02% abundance, 56 (20.9%) differed significantly in abundance according to periodontal disease status. Significant linear correlations for pocket depth and clinical attachment level with bacterial amounts were observed for several taxa. Of the taxa differing in abundance according to periodontal disease status, 53% had multiple oligotypes appearing to differ between none/mild and severe periodontal disease. Among older women, taxonomic differences in subgingival microbiome composition and diversity were observed in relation to clinical periodontal disease measures. Potential differences in bacterial subspecies (oligotypes) and their function were also identified in periodontal disease compared with health.
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Project description:Little is known about longitudinal development of the peri-implant subgingival microbiome and cytokine production as a new sulcus forms after dental implant placement. Therefore, the purpose of this observational study was to evaluate simultaneous longitudinal changes in the oral microbiome and cytokine production in the developing peri-implant sulcus compared to control natural teeth. Four and 12 weeks after implant placement and abutment connection, a dental implant and a natural tooth were sampled in 25 patients for subgingival plaque and gingival crevicular fluid (GCF [around teeth] and peri-implant crevicular fluid [PICF] around implants). DNA from plaque samples was extracted and sequenced using Illumina-based 16S rRNA sequencing. GCF and PICF samples were analyzed using a customized Milliplex human cytokine and chemokine magnetic bead panel. Beta diversity analysis revealed that natural teeth and implants had similar subgingival microbiomes, while teeth had greater alpha diversity than implants. At the genus level, however, few differences were noted between teeth and dental implants over 12 weeks. Specifically, Actinomyces and Selenomonas were significantly elevated around teeth versus dental implants at both 4 weeks and 12 weeks, while Corynebacterium and Campylobacter were significantly elevated only at 4 weeks around teeth. The only difference between PICF and GCF biomarkers was significantly elevated granulocyte-macrophage colony-stimulating factor levels around teeth versus dental implants at the 4-week visit. The subgingival microbiome and cytokine production were similar between teeth and implants during early healing, suggesting that these profiles are driven by the patient following dental implant placement and are not determined by anatomical niche. IMPORTANCE Dental implants are a common treatment option offered to patients for tooth replacement. However, little is known regarding initial colonization of the subgingival microbiome and simultaneous longitudinal cytokine production in humans during the early healing phase following implant placement. We report findings from an in vivo study that assessed initial colonization of the subgingival microbiome and concomitant early cytokine production in a newly formed anatomical space, namely, an implant sulcus. This approach may be useful in future interventional studies to influence dental implant success. Our data showed that the subgingival microbiome and cytokine profile were similar for control natural teeth and dental implants at both 4 and 12 weeks after implant placement. These data suggest that these profiles are driven by the patient and not by anatomical location (i.e., tooth versus dental implant).
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