Dataset Information

The Subgingival Microbiome in Patients with Down Syndrome and Periodontitis.

ABSTRACT: 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.

SUBMITTER: Novoa L

PROVIDER: S-EPMC7463899 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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The Subgingival Microbiome in Patients with Down Syndrome and Periodontitis.

Nóvoa Lourdes L Sánchez María Del Carmen MDC Blanco Juan J Limeres Jacobo J Cuenca Maigualida M Marín María José MJ Sanz Mariano M Herrera David D Diz Pedro P

Journal of clinical medicine 20200802 8

<h4>Objective</h4>To describe the subgingival microbiome of individuals with Down syndrome (DS).<h4>Methods</h4>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 ...[more]

PMID: 32748845

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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.

Project description:Since periodontitis and type 2 diabetes mellitus are complex diseases, a thorough understanding of their pathogenesis requires knowing the relationship of these pathologies with other disorders and environmental factors. In this study, the representability of the subgingival periodontal microbiome of 46 subjects was studied by 16S rRNA gene sequencing and shotgun sequencing of pooled samples. We examined 15 patients with chronic periodontitis (CP), 15 patients with chronic periodontitis associated with type 2 diabetes mellitus (CPT2DM), and 16 healthy subjects (Control). The severity of generalized chronic periodontitis in both periodontitis groups of patients (CP and CPT2DM) was moderate (stage II). The male to female ratios were approximately equal in each group (22 males and 24 females); the average age of the subjects was 53.9 ± 7.3 and 54.3 ± 7.2 years, respectively. The presence of overweight patients (Body Mass Index (BMI) 30-34.9 kg/m2) and patients with class 1-2 obesity (BMI 35-45.9 kg/m2) was significantly higher in the CPT2DM group than in patients having only chronic periodontitis or in the Control group. However, there was no statistically significant difference in all clinical indices between the CP and CPT2DM groups. An analysis of the metagenomic data revealed that the alpha diversity in the CPT2DM group was increased compared to that in the CP and Control groups. The microbiome biomarkers associated with experimental groups were evaluated. In both groups of patients with periodontitis, the relative abundance of Porphyromonadaceae was increased compared to that in the Control group. The CPT2DM group was characterized by a lower relative abundance of Streptococcaceae/Pasteurellaceae and a higher abundance of Leptotrichiaceae compared to those in the CP and Control groups. Furthermore, the CP and CPT2DM groups differed in terms of the relative abundance of Veillonellaceae (which was decreased in the CPT2DM group compared to CP) and Neisseriaceae (which was increased in the CPT2DM group compared to CP). In addition, differences in bacterial content were identified by a combination of shotgun sequencing of pooled samples and genome-resolved metagenomics. The results indicate that there are subgingival microbiome-specific features in patients with chronic periodontitis associated with type 2 diabetes mellitus.

Project description:Chronic periodontitis is an inflammatory disease of the periodontium affecting nearly 65 million adults in the United States. Changes in subgingival microbiota have long been associated with chronic periodontitis. Recent culture-independent molecular studies have revealed the immense richness and complexity of oral microbial communities. However, data sets across studies have not been directly compared, and whether the observed microbial variations are consistent across different studies is not known. Here, we used 16S rRNA sequencing to survey the subgingival microbiota in 25 subjects with chronic periodontal disease and 25 healthy controls and compared our data sets with those of three previously reported microbiome studies. Consistent with data from previous studies, our results demonstrate a significantly altered microbial community structure with decreased heterogeneity in periodontal disease. Comparison with data from three previously reported studies revealed that subgingival microbiota clustered by study. However, differences between periodontal health and disease were larger than the technical variations across studies. Using a prediction score and applying five different distance metrics, we observed two predominant clusters. One cluster was driven by Fusobacterium and Porphyromonas and was associated with clinically apparent periodontitis, and the second cluster was dominated by Rothia and Streptococcus in the majority of healthy sites. The predicted functional capabilities of the periodontitis microbiome were significantly altered. Genes involved in bacterial motility, energy metabolism, and lipopolysaccharide biosynthesis were overrepresented in periodontal disease, whereas genes associated with transporters, the phosphotransferase system, transcription factors, amino acid biosynthesis, and glycolysis/gluconeogenesis were enriched in healthy controls. These results demonstrate significant alterations in microbial composition and function in periodontitis and suggest genes and metabolic pathways associated with periodontal disease.

Project description:Background and objectiveAggressive periodontitis (AgP) is prevalent and shows a rapid course in African individuals. Although a strong focus has been placed on Aggregatibacter actinomycetemcomitans, new methods support the existence of a complex subgingival microflora in AgP. The purpose of the present study was to map the subgingival microbiota as well as explore the presence of A. actinomycetemcomitans and the JP2 clone in a group of Sudanese individuals with AgP, using different analytical methods.Material and methodsA study population consisting of 19 patients with AgP was recruited from patients seeking treatment at University of Science and Technology (UST) in Khartoum. Fifteen healthy subjects were included as controls. Plaque samples were analyzed for 272 taxa using human oral microbe identification microarrays and for 26 periodontal taxa using DNA-DNA hybridization checkerboard. Conventional polymerase chain reaction (PCR) was applied for the detection of A. actinomycetemcomitans and the JP2 clone in plaque. Saliva from patients with AgP was analyzed using quantitative PCR (qPCR) for the detection of A. actinomycetemcomitans.ResultsEubacterium yurii was detected more frequently in patients with AgP than in controls, and E. nodatum was found in patients with AgP only. A. actinomycetemcomitans was found in plaque samples of two (12%) patients by human oral microbe identification microarrays and in five (29%) patients with AgP by conventional PCR, as well as in six (32%) of the AgP saliva samples by qPCR. The JP2 clone was identified in only one patient.ConclusionThe classical periodontal pathogens were not present in high amounts in AgP in the population studied here. Species of Eubacterium, which are not typically associated with AgP, were often detected in individuals with disease. Using laboratory methods with different sensitivities and detection levels allowed identification of variances in microbial communities. The findings reported in this study provide a basis for the further understanding of AgP.

Project description:UNLABELLED:The human microbiome influences and reflects the health or disease state of the host. Periodontitis, a disease affecting about half of American adults, is associated with alterations in the subgingival microbiome of individual tooth sites. Although it can be treated, the disease can reoccur and may progress without symptoms. Without prognostic markers, follow-up examinations are required to assess reoccurrence and disease progression and to determine the need for additional treatments. To better identify and predict the disease progression, we aim to determine whether the subgingival microbiome can serve as a diagnosis and prognosis indicator. Using metagenomic shotgun sequencing, we characterized the dynamic changes in the subgingival microbiome in periodontitis patients before and after treatment at the same tooth sites. At the taxonomic composition level, the periodontitis-associated microorganisms were significantly shifted from highly correlated in the diseased state to poorly correlated after treatment, suggesting that coordinated interactions among the pathogenic microorganisms are essential to disease pathogenesis. At the functional level, we identified disease-associated pathways that were significantly altered in relative abundance in the two states. Furthermore, using the subgingival microbiome profile, we were able to classify the samples to their clinical states with an accuracy of 81.1%. Follow-up clinical examination of the sampled sites supported the predictive power of the microbiome profile on disease progression. Our study revealed the dynamic changes in the subgingival microbiome contributing to periodontitis and suggested potential clinical applications of monitoring the subgingival microbiome as an indicator in disease diagnosis and prognosis. IMPORTANCE:Periodontitis is a common oral disease. Although it can be treated, the disease may reoccur without obvious symptoms. Current clinical examination parameters are useful in disease diagnosis but cannot adequately predict the outcome of individual tooth sites after treatment. A link between the subgingival microbiota and periodontitis was identified previously; however, it remains to be investigated whether the microbiome can serve as a diagnostic and prognostic indicator. In this study, for the first time, we characterized the subgingival microbiome of individual tooth sites before and after treatment using a large-scale metagenomic analysis. Our longitudinal study revealed changes in the microbiota in taxonomic composition, cooccurrence of subgingival microorganisms, and functional composition. Using the microbiome profiles, we were able to classify the clinical states of subgingival plaque samples with a high accuracy. Follow-up clinical examination of sampled sites indicates that the subgingival microbiome profile shows promise for the development of diagnostic and prognostic tools.

Dataset Information

The Subgingival Microbiome in Patients with Down Syndrome and Periodontitis.

Publications

The Subgingival Microbiome in Patients with Down Syndrome and Periodontitis.

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