Dataset Information

Subgingival microbiome in patients with healthy and ailing dental implants.

ABSTRACT: Dental implants are commonly used to replace missing teeth. However, the dysbiotic polymicrobial communities of peri-implant sites are responsible for peri-implant diseases, such as peri-implant mucositis and peri-implantitis. In this study, we analyzed the microbial characteristics of oral plaque from peri-implant pockets or sulci of healthy implants (n = 10), peri-implant mucositis (n = 8) and peri-implantitis (n = 6) sites using pyrosequencing of the 16S rRNA gene. An increase in microbial diversity was observed in subgingival sites of ailing implants, compared with healthy implants. Microbial co-occurrence analysis revealed that periodontal pathogens, such as Porphyromonas gingivalis, Tannerella forsythia, and Prevotella intermedia, were clustered into modules in the peri-implant mucositis network. Putative pathogens associated with peri-implantitis were present at a moderate relative abundance in peri-implant mucositis, suggesting that peri-implant mucositis an important early transitional phase during the development of peri-implantitis. Furthermore, the relative abundance of Eubacterium was increased at peri-implantitis locations, and co-occurrence analysis revealed that Eubacterium minutum was correlated with Prevotella intermedia in peri-implantitis sites, which suggests the association of Eubacterium with peri-implantitis. This study indicates that periodontal pathogens may play important roles in the shifting of healthy implant status to peri-implant disease.

SUBMITTER: Zheng H

PROVIDER: S-EPMC4468443 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Subgingival microbiome in patients with healthy and ailing dental implants.

Zheng Hui H Xu Lixin L Wang Zicheng Z Li Lianshuo L Zhang Jieni J Zhang Qian Q Chen Ting T Lin Jiuxiang J Chen Feng F

Scientific reports 20150616

Dental implants are commonly used to replace missing teeth. However, the dysbiotic polymicrobial communities of peri-implant sites are responsible for peri-implant diseases, such as peri-implant mucositis and peri-implantitis. In this study, we analyzed the microbial characteristics of oral plaque from peri-implant pockets or sulci of healthy implants (n = 10), peri-implant mucositis (n = 8) and peri-implantitis (n = 6) sites using pyrosequencing of the 16S rRNA gene. An increase in microbial di ...[more]

PMID: 26077225

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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).

Project description:ObjectivesTo evaluate the colonization dynamics of subgingival microbiota established over six months around newly installed dental implants in periodontally healthy individuals, compared with their corresponding teeth.MethodologySeventeen healthy individuals assigned to receive single dental implants participated in the study. Subgingival biofilm was sampled from all implant sites and contralateral/ antagonist teeth on days 7, 30, 90, and 180 after implant installation. Microbiological analysis was performed using the Checkerboard DNA-DNA hybridization technique for detection of classical oral taxa and non-oral microorganisms. Significant differences were estimated by Mann-Whitney and Friedman tests, while associations between implants/teeth and target species levels were assessed by linear regression analysis (LRA). Significance level was set at 5%.ResultsLevels of some species were significantly higher in teeth compared to implants, respectively, at day 7 ( V.parvula , 6 × 10 5 vs 3 × 105 ; Milleri streptococci , 2 × 10 6 vs 6 × 10 5 ; Capnocytophaga spp., 2 × 10 6 vs 9 × 10 5 ; E.corrodens , 2 × 10 6 vs 5 × 10 5 ; N. mucosa , 2 × 10 6 vs 5 × 10 5 ; S.noxia , 2 × 10 6 vs 3 × 10 5 ; T.socranskii , 2 × 10 6 vs 5 × 10 5 ; H.alvei , 4 × 10 5 vs 2 × 10 5 ; and Neisseria spp., 6 × 10 5 vs 4 × 10 4 ), day 30 ( V.parvula , 5 × 10 5 vs 10 5 ; Capnocytophaga spp., 1.3 × 10 6 vs 6.8 × 10 4 ; F.periodonticum , 2 × 10 6 vs 10 6 ; S.noxia , 6 × 10 5 vs 2 × 10 5 ; H.alvei , 8 × 10 5 vs 9 × 10 4 ; and Neisseria spp., 2 × 10 5 vs 10 6 ), day 120 ( V.parvula , 8 × 10 5 vs 3 × 10 5 ; S.noxia , 2 × 10 6 vs 0; and T.socranskii , 3 × 10 5 vs 8 × 10 4 ), and day 180 ( S.enterica subsp. enterica serovar Typhi, 8 × 10 6 vs 2 × 10 6 ) (p<0.05). Implants showed significant increases over time in the levels of F.nucleatum , Gemella spp., H.pylori , P.micra , S.aureus , S.liquefaciens , and T.forsythia (p<0.05). LRA found that dental implants were negatively correlated with high levels of S. noxia and V. parvula (β=-0.5 to -0.3; p<0.05).ConclusionsEarly submucosal microbiota is diverse and only a few species differ between teeth and implants in the same individual. Only 7 days after implant installation, a rich microbiota can be found in the peri-implant site. After six months of evaluation, teeth and implants show similar prevalence and levels of the target species, including known and new periodontopathic species.

Project description:Background: Oral microbiota are considered major players in the development of periodontal diseases. Thorough knowledge of intact subgingival microbiomes is required to elucidate microbial shifts from health to disease. Aims: This comparative study investigated the subgingival microbiome of healthy children, possible inter- and intra-individual effects of modified sampling, and basic comparability of subgingival microprints. Methods: In five 10-year-old children, biofilm was collected from the upper first premolars and first molars using sterilized, UV-treated paper-points inserted into the subgingival sulcus at eight sites. After supragingival cleaning using an electric toothbrush and water, sampling was performed, firstly, excluding (Mode A) and, secondly, including (Mode B) cleansing with sterile cotton pellets. DNA was extracted from the pooled samples, and primers targeting 16S rRNA hypervariable regions V5 and V6 were used for 454-pyrosequencing. Wilcoxon signed rank test and t-test were applied to compare sampling modes. Principal coordinate analysis (PCoA) and average agglomerative hierarchical clustering were calculated with unweighted UniFrac distance matrices. Sample grouping was tested with permutational MANOVA (Adonis). Results: Data filtering and quality control yielded 67,218 sequences with an average sequence length of 243bp (SD 6.52; range 231-255). Actinobacteria (2.8-24.6%), Bacteroidetes (9.2-25.1%), Proteobacteria (4.9-50.6%), Firmicutes (16.5-57.4%), and Fusobacteria (2.2-17.1%) were the five major phyla found in all samples. Differences in microbial abundances between sampling modes were not evident. High sampling numbers are needed to achieve significance for rare bacterial phyla. Samples taken from one individual using different sampling modes were more similar to each other than to other individuals' samples. PCoA and hierarchical clustering showed a grouping of the paired samples. Permutational MANOVA did not reveal sample grouping by sampling modes (p = 0.914 by R2 = 0.09). Conclusion: A slight modification of sampling mode has minor effects corresponding to a natural variability in the microbiome profiles of healthy children. The inter-individual variability in subgingival microprints is greater than intra-individual differences. Statistical analyses of microbial populations should consider this baseline variability and move beyond mere quantification with input from visual analytics. Comparative results are difficult to summarize as methods for studying huge datasets are still evolving. Advanced approaches are needed for sample size calculations in clinical settings.

Dataset Information

Subgingival microbiome in patients with healthy and ailing dental implants.

Publications

Subgingival microbiome in patients with healthy and ailing dental implants.

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