The subgingival microbiome of clinically healthy current and never smokers.
ABSTRACT: Dysbiotic oral bacterial communities have a critical role in the etiology and progression of periodontal diseases. The goal of this study was to investigate the extent to which smoking increases risk for disease by influencing the composition of the subgingival microbiome in states of clinical health. Subgingival plaque samples were collected from 200 systemically and periodontally healthy smokers and nonsmokers. 16S pyrotag sequencing was preformed generating 1,623,713 classifiable sequences, which were compared with a curated version of the Greengenes database using the quantitative insights into microbial ecology pipeline. The subgingival microbial profiles of smokers and never-smokers were different at all taxonomic levels, and principal coordinate analysis revealed distinct clustering of the microbial communities based on smoking status. Smokers demonstrated a highly diverse, pathogen-rich, commensal-poor, anaerobic microbiome that is more closely aligned with a disease-associated community in clinically healthy individuals, suggesting that it creates an at-risk-for-harm environment that is primed for a future ecological catastrophe.
Project description:Although smoking and diabetes have been established as the only two risk factors for periodontitis, their individual and synergistic impacts on the periodontal microbiome are not well studied. The present investigation analyzed 2.7 million 16S sequences from 175 non-smoking normoglycemic individuals (controls), smokers, diabetics and diabetic smokers with periodontitis as well as periodontally healthy controls, smokers and diabetics to assess subgingival bacterial biodiversity and co-occurrence patterns. The microbial signatures of periodontally healthy smokers, but not diabetics, were highly aligned with the disease-associated microbiomes of their respective cohorts. Diabetics were dominated by species belonging to Fusobacterium, Parvimonas, Peptostreptococcus, Gemella, Streptococcus, Leptotrichia, Filifactor, Veillonella, TM7 and Terrahemophilus. These microbiomes exhibited significant clustering based on HbA1c levels (pre-diabetic (<6.5%), diabetic (6.5-9.9%), diabetics >10%). Smokers with periodontitis evidenced a robust core microbiome (species identified in at least 80% of individuals) dominated by anaerobes, with inter-individual differences attributable largely to the 'rare biosphere'. Diabetics and diabetic smokers, on the other hand, were microbially heterogeneous and enriched for facultative species. In smokers, microbial co-occurrence networks were sparse and predominantly congeneric, while robust inter-generic networks were observed in diabetics and diabetic smokers. Smoking and hyperglycemia impact the subgingival microbiome in distinct ways, and when these perturbations intersect, their synergistic effect is greater than what would be expected from the sum of each effect separately. Thus, this study underscores the importance of early intervention strategies in maintaining health-compatible microbiomes in high-risk individuals, as well as the need to personalize these interventions based on the environmental perturbation.
Project description:<h4>Objective</h4>Rheumatoid arthritis (RA) and periodontitis are chronic inflammatory diseases that share common risk factors. However, the bidirectional relationship between RA and periodontal disease is not fully understood. This study was undertaken to describe the bacterial component of the subgingival microbiome in RA patients and to relate this to RA disease activity and periodontal status.<h4>Methods</h4>Patients with chronic established RA (N = 78) were periodontally examined and their subgingival plaque samples were collected; their clinical and laboratory data on RA status and medication were obtained from medical records. Bacterial DNA was quantified by universal 16S rDNA qPCR, and Porphyromonas gingivalis by species-specific qPCR. For microbiome assessment, 16S rDNA amplicon sequencing was performed.<h4>Results</h4>Active RA was diagnosed in 58% of the patients and periodontitis in 82% (mild: 9%, moderate: 55%, severe: 18%). P. gingivalis was present in 14% of the samples. Different levels of gingival bleeding, periodontal probing depth, RA disease status, prednisolone use and smoking were associated with significantly different microbiome compositions. Two subgingival microbial community types were discerned.<h4>Conclusion</h4>In RA patients with active disease, anti-inflammatory medication as part of RA therapy was associated with better oral health status and a healthier subgingival microbiome compared to that of RA patients in remission, especially those in remission who were current smokers. RA patients in remission with current smoking status may particularly benefit from a systematic periodontal treatment program. The potential role of microbial community types in patient stratification and personalized therapy should be assessed in longitudinal studies.
Project description:Smoking is a risk factor for periodontal disease, and a cause of oral microbiome dysbiosis. While this has been evaluated for traditional cigarette smoking, there is limited research on the effect of other tobacco types on the oral microbiome. This study investigates subgingival microbiome composition in smokers of different tobacco types and their effect on periodontal health. Subgingival plaques were collected from 40 individuals, including smokers of either cigarettes, medwakh, or shisha, and non-smokers seeking dental treatment at the University Dental Hospital in Sharjah, United Arab Emirates. The entire (~?1500 bp) 16S rRNA bacterial gene was fully amplified and sequenced using Oxford Nanopore technology. Subjects were compared for the relative abundance and diversity of subgingival microbiota, considering smoking and periodontal condition. The relative abundances of several pathogens were significantly higher among smokers, such as Prevotella denticola and Treponema sp. OMZ 838 in medwakh smokers, Streptococcus mutans and Veillonella dispar in cigarette smokers, Streptococcus sanguinis and Tannerella forsythia in shisha smokers. Subgingival microbiome of smokers was altered even in subjects with no or mild periodontitis, probably making them more prone to severe periodontal diseases. Microbiome profiling can be a useful tool for periodontal risk assessment. Further studies are recommended to investigate the impact of tobacco cessation on periodontal disease progression and oral microbiome.
Project description:OBJECTIVE:Studies that demonstrate an association between rheumatoid arthritis (RA) and dysbiotic oral microbiomes are often confounded by the presence of extensive periodontitis in these individuals. This study was undertaken to investigate the role of RA in modulating the periodontal microbiome by comparing periodontally healthy individuals with RA to those without RA. METHODS:Subgingival plaque was collected from periodontally healthy individuals (22 with RA and 19 without RA), and the 16S gene was sequenced on an Illumina MiSeq platform. Bacterial biodiversity and co-occurrence patterns were examined using the QIIME and PhyloToAST pipelines. RESULTS:The subgingival microbiomes differed significantly between patients with RA and controls based on both community membership and the abundance of lineages, with 41.9% of the community differing in abundance and 19% in membership. In contrast to the sparse and predominantly congeneric co-occurrence networks seen in controls, RA patients revealed a highly connected grid containing a large intergeneric hub anchored by known periodontal pathogens. Predictive metagenomic analysis (PICRUSt) demonstrated that arachidonic acid and ester lipid metabolism pathways might partly explain the robustness of this clustering. As expected from a periodontally healthy cohort, Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans were not significantly different between groups; however, Cryptobacterium curtum, another organism capable of producing large amounts of citrulline, emerged as a robust discriminant of the microbiome in individuals with RA. CONCLUSION:Our data demonstrate that the oral microbiome in RA is enriched for inflammophilic and citrulline-producing organisms, which may play a role in the production of autoantigenic citrullinated peptides in RA.
Project description:Periodontal microorganisms not only colonize subgingival pockets, but also are detected on various mucous membranes in patients with periodontitis. The object of this pilot study was, using the next-generation sequencing of 16S RNA gene, to characterize the microbiota in two oral habitats (buccal mucosas and subgingival pockets) in patients with different forms of periodontitis. Thirty-two buccal swab samples and 113 subgingival samples were obtained from eleven subjects with chronic periodontitis (ChP), twelve subjects with aggressive periodontitis (AgP), and nine periodontally healthy individuals (HP). Using Miseq Sequencing of 16S rRNA gene, we found that the subgingival and buccal mucosa microbiome of ChP and AgP patients both differed from HP. Meanwhile, Veillonella, Treponema, Filifactor, Fretibacterium, Peptostreptococcaceae_[XI][G-6], Peptostreptococcaceae_[XI][G-5], Bacteroidetes_[G-5], Bacteroidetes_[G-3], Peptostreptococcaceae_[XI][G-4], Peptostreptococcaceae_[XI][G-2] significantly increased both in buccal and subgingival plaque samples in periodontitis subjects (ChP and AgP) compared with HP. Moreover, the results based on the Unweighted UniFrac distance showed that buccal and subgingival plaque samples from the same individuals show higher community divergence than same habitats from different subject samples. This study demonstrated that the microbiome of buccal mucosa can be influenced by periodontitis. However, subgingival and buccal mucosa microbiome seem to be characterized by species-specific colonization patterns. This pilot study provides a glimpse at the changes of subgingival and buccal mucosa associated with periodontitis from a holistic view. Further studies should be taken to illuminate the interplay between these detected changes and periodontitis development.
Project description:Leukocyte Adhesion Deficiency I (LAD-I) is a primary immunodeficiency caused by single gene mutations in the CD18 subunit of ?2 integrins which result in defective transmigration of neutrophils into the tissues. Affected patients suffer from recurrent life threatening infections and severe oral disease (periodontitis). Microbial communities in the local environment (subgingival plaque) are thought to be the triggers for inflammatory periodontitis, yet little is known regarding the microbial communities associated with LAD-I periodontitis. Here we present the first comprehensive characterization of the subgingival communities in LAD-I, using a 16S rRNA gene-based microarray, and investigate the relationship of this tooth adherent microbiome to the local immunopathology of periodontitis. We show that the LAD subgingival microbiome is distinct from that of health and Localized Aggressive Periodontitits. Select periodontitis-associated species in the LAD microbiome included Parvimonas micra, Porphyromonas endodontalis, Eubacterium brachy and Treponema species. Pseudomonas aeruginosa, a bacterium not typically found in subgingival plaque is detected in LAD-I. We suggest that microbial products from LAD-associated communities may have a role in stimulating the local inflammatory response. We demonstrate that bacterial LPS translocates into the lesions of LAD-periodontitis potentially triggering immunopathology. We also show in in vitro assays with human macrophages and in vivo in animal models that microbial products from LAD-associated subgingival plaque trigger IL-23-related immune responses, which have been shown to dominate in patient lesions. In conclusion, our current study characterizes the subgingival microbial communities in LAD-periodontitis and supports their role as triggers of disease pathogenesis.
Project description:Periodontitis is a kind of infectious disease initiated by colonization of subgingival periodontal pathogens, which cause destruction of tooth-supporting tissues, and is a predominant threat to oral health as the most common cause of loss of teeth. The aim of this pilot study was to characterize the subgingival bacterial biodiversity of periodontal pockets with different probing depths in patients with different forms of periodontitis. Twenty-one subgingival plaque samples were collected from three patients with chronic periodontitis (ChP), three patients with aggressive periodontitis (AgP) and three periodontally healthy subjects (PH). Each patient with periodontitis was sampled at three sites, at different probing depths (PDs, one each at 4 mm, 5-6 mm, and ≥ 7 mm). Using 16S rRNA gene high-throughput sequencing and bioinformatic analysis, we found that subgingival communities in health and periodontitis samples largely differed. Meanwhile, Acholeplasma, Fretibacterium, Porphyromonas, Peptococcus, Treponema_2, Defluviitaleaceae_UCG_011, Filifactor, and Mycoplasma increased with the deepening of the pockets in ChP, whilst only Corynebacterium was negatively associated with PD. In AgP, Corynebacterium and Klebsiella were positively associated with PD, while Serratia, Pseudoramibacter, Defluviitaleaceae_UCG_011, and Desulfobulbus were negatively associated with PD. And among these two groups, Corynebacterium shifted differently. Moreover, in subgingival plaque, the unweighted UniFrac distances between samples from pockets with different PD in the same patients were significantly lower than those from pockets within the same PD category from different patients. This study demonstrated the shift of the subgingival microbiome in individual teeth sites during disease development. Within the limitation of the relative small sample size, this pilot study shed new light on the dynamic relationship between the extent of periodontal destruction and the subgingival microbiome.
Project description:The etiology of periodontitis has traditionally been associated to a consortium of three bacterial species-the so-called "red-complex" of periodontal disease-which has been the target for most diagnostic and therapeutic strategies. However, other species have also been found to correlate with disease severity. In addition, the influence of smoking on periodontal microbiota is poorly understood. In the current manuscript, the composition of the subgingival microbiota in healthy individuals vs. patients with chronic periodontitis has been investigated using 16S pyrosequencing and the influence of smoking on periodontal composition has been examined. Subgingival bacterial communities were sampled from 82 patients: 22 non-smoking healthy controls, 28 non-smoking periodontal patients, and 32 smoking periodontal patients. Bacterial diversity was higher in periodontal patients than in healthy subjects, which could be interpreted as the consequence of a nutritionally richer environment or a reduced immune competence. Periodontal patients showed a significantly higher prevalence/relative abundance of "established" periopathogens but also other taxa whose role is not well-established and that should be targets for future research. These include Anaeroglobus, Bulleidia, Desulfobulbus, Filifactor, Mogibacterium, Phocaeicola, Schwartzia or TM7. The microbial community of smoking-associated periodontitis is less diverse and distinct from that of non-smokers, indicating that smoking has an influence on periodontal ecology. Interestingly, the high sequencing coverage allowed the detection at low proportions of periodontal pathogens in all healthy individuals, indicating that chronic periodontitis cannot be strictly considered an infectious disease but the outcome of a polymicrobial dysbiosis, where changes in the proportions of microbial consortia trigger the inflammatory and tissue-degradation responses of the host.
Project description:Periodontally involved teeth have been implicated as 'microbial reservoirs' in the etiology of peri-implant diseases. Therefore, the purpose of this investigation was to use a deep-sequencing approach to identify the degree of congruence between adjacent peri-implant and periodontal microbiomes in states of health and disease. Subgingival and peri-implant biofilm samples were collected from 81 partially edentulous individuals with periodontal and peri-implant health and disease. Bacterial DNA was isolated, and the 16S rRNA gene was amplified and sequenced by pyrotag sequencing. Chimera-depleted sequences were compared against a locally hosted curated database for bacterial identification. Statistical significance was determined by paired Student's t tests between tooth-implant pairs. The 1.9 million sequences identified represented 523 species. Sixty percent of individuals shared less than 50% of all species between their periodontal and peri-implant biofilms, and 85% of individuals shared less than 8% of abundant species between tooth and implant. Additionally, the periodontal microbiome demonstrated significantly higher diversity than the implant, and distinct bacterial lineages were associated with health and disease in each ecosystem. Analysis of our data suggests that simple geographic proximity is not a sufficient determinant of colonization of topographically distinct niches, and that the peri-implant and periodontal microbiomes represent microbiologically distinct ecosystems.
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.