The Role of Oral Pathobionts in Dysbiosis during Periodontitis Development.
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ABSTRACT:
SUBMITTER: Jiao Y
PROVIDER: S-EPMC4023464 | biostudies-literature | 2014 Jun
REPOSITORIES: biostudies-literature
Publications
The Role of Oral Pathobionts in Dysbiosis during Periodontitis Development.
Jiao Y Y Hasegawa M M Inohara N N
Journal of dental research 20140319 6
An emerging concept is the tight relationship between dysbiosis (microbiota imbalance) and disease. The increase in knowledge about alterations in microbial communities that reside within the host has made a strong impact not only on dental science, but also on immunology and microbiology as well as on our understanding of several diseases. Periodontitis is a well-characterized human disease associated with dysbiosis, characterized by the accumulation of multiple bacteria that play individual an ...[more]
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Project description:ObjectivesWe have previously characterized the main osteoimmunological events that occur during ligature periodontitis. This study aims to determine the polymicrobial community shifts that occur during disease development.MethodsPeriodontitis was induced in C57BL/6 mice using the ligature-induced periodontitis model. Healthy oral mucosa swabs and ligatures were collected every 3 days from 0 to 18 days post-ligature placement. Biofilm samples were evaluated by 16SrRNA gene sequencing (Illumina MiSeq) and QIIME. Time-course changes were determined by relative abundance, diversity, and rank analyses (PERMANOVA, Bonferroni-adjusted).ResultsMicrobial differences between health and periodontal inflammation were observed at all phylogenic levels. An evident microbial community shift occurred in 25 genera during the advancement of "gingivitis" (3-6 days) to periodontitis (9-18 days). From day 0 to 18, dramatic changes were identified in Streptococcus levels, with an overall decrease (54.04%-0.02%) as well an overall increase of Enterococcus and Lactobacillus (23.7%-73.1% and 10.1%-70.2%, respectively). Alpha-diversity decreased to its lowest at 3 days, followed by an increase in diversity as disease advancement. Beta-diversity increased after ligature placement, indicating that bone loss develops in response to a greater microbial variability (p = 0.001). Levels of facultative and strict anaerobic bacteria augmented over the course of disease progression, with a total of eight species significantly different during the 18-day period.ConclusionThe data supports that murine gingival inflammation and alveolar bone loss develop in response to microbiome shifts. Bacterial diversity increased during progression to bone loss. These findings further support the utilization of the periodontitis ligature model for microbial shift analysis under different experimental conditions.
| S-EPMC10034670 | biostudies-literature
Project description:Periodontitis is a polymicrobial biofilm-induced inflammatory disease that affects 743 million people worldwide. The current model to explain periodontitis progression proposes that changes in the relative abundance of members of the oral microbiome lead to dysbiosis in the host-microbiome crosstalk and then to inflammation and bone loss. Using combined metagenome/metatranscriptome analysis of the subgingival microbiome in progressing and non-progressing sites, we have characterized the distinct molecular signatures of periodontitis progression.Metatranscriptome analysis was conducted on samples from subgingival biofilms from progressing and stable sites from periodontitis patients. Community-wide expression profiles were obtained using Next Generation Sequencing (Illumina). Sequences were aligned using 'bowtie2' against a constructed oral microbiome database. Differential expression analysis was performed using the non-parametric algorithm implemented on the R package 'NOISeqBio'. We summarized global functional activities of the oral microbial community by set enrichment analysis based on the Gene Ontology (GO) orthology.Gene ontology enrichment analysis showed an over-representation in the baseline of active sites of terms related to cell motility, lipid A and peptidoglycan biosynthesis, and transport of iron, potassium, and amino acids. Periodontal pathogens (Tannerella forsythia and Porphyromonas gingivalis) upregulated different TonB-dependent receptors, peptidases, proteases, aerotolerance genes, iron transport genes, hemolysins, and CRISPR-associated genes. Surprisingly, organisms that have not been usually associated with the disease (Streptococcus oralis, Streptococcus mutans, Streptococcus intermedius, Streptococcus mitis, Veillonella parvula, and Pseudomonas fluorenscens) were highly active transcribing putative virulence factors. We detected patterns of activities associated with progression of clinical traits. Among those we found that the profiles of expression of cobalamin biosynthesis, proteolysis, and potassium transport were associated with the evolution towards disease.We identified metabolic changes in the microbial community associated with the initial stages of dysbiosis. Regardless of the overall composition of the community, certain metabolic signatures are consistent with disease progression. Our results suggest that the whole community, and not just a handful of oral pathogens, is responsible for an increase in virulence that leads to progression.NCT01489839, 6 December 2011.
| S-EPMC4410737 | biostudies-literature
Project description:Infection with specific pathogens and alterations in tissue commensal microbial composition are intricately associated with the development of many human cancers. Likewise, dysbiosis of oral microbiome was also shown to play critical role in the initiation as well as progression of oral cancer. However, there are no reports portraying changes in oral microbial community in the patients of Indian subcontinent, which has the highest incidence of oral cancer per year, globally. To establish the association of bacterial dysbiosis and oral squamous cell carcinoma (OSCC) among the Indian population, malignant lesions and anatomically matched adjacent normal tissues were obtained from fifty well-differentiated OSCC patients and analyzed using 16S rRNA V3-V4 amplicon based sequencing on the MiSeq platform. Interestingly, in contrast to the previous studies, a significantly lower bacterial diversity was observed in the malignant samples as compared to the normal counterpart. Overall our study identified Prevotella, Corynebacterium, Pseudomonas, Deinococcus and Noviherbaspirillum as significantly enriched genera, whereas genera including Actinomyces, Sutterella, Stenotrophomonas, Anoxybacillus, and Serratia were notably decreased in the OSCC lesions. Moreover, we demonstrated HPV-16 but not HPV-18 was significantly associated with the OSCC development. In future, with additional validation, this panel could directly be applied into clinical diagnostic and prognostic workflows for OSCC in Indian scenario.
| S-EPMC7940518 | biostudies-literature
Project description:Oral squamous cell carcinoma (OSCC) is the leading cause of mortality for oral cancer. Numerous risk factors mainly related to unhealthy habits and responsible for chronic inflammation and infections have been recognized as predisposing factors for oral carcinogenesis. Recently, even microbiota alterations have been associated with the development of human cancers. In particular, some specific bacterial strains have been recognized and strongly associated with oral cancer development (Capnocytophaga gingivalis, Fusobacterium spp., Streptococcus spp., Peptostreptococcus spp., Porphyromonas gingivalis and Prevotella spp.). Several hypotheses have been proposed to explain how the oral microbiota could be involved in cancer pathogenesis by mainly paying attention to chronic inflammation, microbial synthesis of cancerogenic substances, and alteration of epithelial barrier integrity. Based on knowledge of the carcinogenic effects of dysbiosis, it was recently suggested that probiotics may have anti-tumoral activity. Nevertheless, few data exist with regard to probiotic effects on oral cancer. On this basis, the association between the development of oral cancer and oral dysbiosis is discussed focusing attention on the potential benefits of probiotics administration in cancer prevention.
| S-EPMC7079586 | biostudies-literature
Project description:BackgroundOral microbiota is closely related to the homeostasis of the oral cavity and lungs. To provide potential information for the prediction, screening, and treatment strategies of individuals, this study compared and investigated the bacterial signatures in periodontitis and chronic obstructive pulmonary disease (COPD).Materials and methodsWe collected subgingival plaque and gingival crevicular fluid samples from 112 individuals (31 healthy controls, 24 patients with periodontitis, 28 patients with COPD, and 29 patients with both periodontitis and COPD). The oral microbiota was analyzed using 16S rRNA gene sequencing and diversity and functional prediction analysis were performed.ResultsWe observed higher bacterial richness in individuals with periodontitis in both types of oral samples. Using LEfSe and DESeq2 analyses, we found differentially abundant genera that may be potential biomarkers for each group. Mogibacterium is the predominant genus in COPD. Ten genera, including Desulfovibrio, Filifactor, Fretibacterium, Moraxella, Odoribacter, Pseudoramibacter Pyramidobacter, Scardovia, Shuttleworthia and Treponema were predominant in periodontitis. Bergeyella, Lautropia, Rothia, Propionibacterium and Cardiobacterium were the signature of the healthy controls. The significantly different pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) between healthy controls and other groups were concentrated in genetic information processing, translation, replication and repair, and metabolism of cofactors and vitamins.ConclusionsWe found the significant differences in the bacterial community and functional characterization of oral microbiota in periodontitis, COPD and comorbid diseases. Compared to gingival crevicular fluid, subgingival plaque may be more appropriate for reflecting the difference of subgingival microbiota in periodontitis patients with COPD. These results may provide potentials for predicting, screening, and treatment strategies for individuals with periodontitis and COPD.
| S-EPMC9948037 | biostudies-literature
Project description:Inflammation is a driven force in modulating microbial communities, but little is known about the interplay between colonizing microorganisms and the immune response in periodontitis. Since local and systemic inflammation may play a whole role in disease, we aimed to evaluate the oral and fecal microbiome of patients with periodontitis and to correlate the oral microbiome data with levels of inflammatory mediator in saliva. Methods: Nine patients with periodontitis (P) in Stage 3/Grade B and nine age-matched non-affected controls (H) were evaluated. Microbial communities of oral biofilms (the supra and subgingival from affected and non-affected sites) and feces were determined by sequencing analysis of the 16SrRNA V3-V4 region. Salivary levels of 40 chemokines and cytokines were correlated with oral microbiome data. Results: Supragingival microbial communities of P differed from H (Pielou's evenness index, and Beta diversity, and weighted UniFrac), since relative abundance (RA) of Defluviitaleaceae, Desulfobulbaceae, Mycoplasmataceae, Peptostreococcales-Tissierellales, and Campylobacteraceae was higher in P, whereas Muribaculaceae and Streptococcaceae were more abundant in H. Subgingival non-affected sites of P did not differ from H, except for a lower abundance of Gemellaceae. The microbiome of affected periodontitis sites (PD ≥ 4 mm) clustered apart from the subgingival sites of H. Oral pathobionts was more abundant in sub and supragingival biofilms of P than H. Fecal samples of P were enriched with Acidaminococcus, Clostridium, Lactobacillus, Bifidobacterium, Megasphaera, and Romboutsia when compared to H. The salivary levels of interleukin 6 (IL-6) and inflammatory chemokines were positively correlated with the RA of several recognized and putative pathobionts, whereas the RA of beneficial species, such as Rothia aeria and Haemophilus parainfluenzae was negatively correlated with the levels of Chemokine C-C motif Ligand 2 (CCL2), which is considered protective. Dysbiosis in patients with periodontitis was not restricted to periodontal pockets but was also seen in the supragingival and subgingival non-affected sites and feces. Subgingival dysbiosis revealed microbial signatures characteristic of different immune profiles, suggesting a role for candidate pathogens and beneficial organisms in the inflammatory process of periodontitis.
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Project description:This article provides supporting data for the research article 'Microbial Signatures of Oral Dysbiosis, Periodontitis and Edentulism Revealed by Gene Meter Methodology' (M.C. Hunter, A.E. Pozhitkov, P.A. Noble, 2016) [1]. In that article, we determined the microbial abundance signatures for patient with periodontics, edentulism, or health using Gene Meter Technology. Here we provide the data used to make the DNA microarray and the resulting microbial abundance data that was determined using the calibrated probes and the 16S rRNA genes harvested from patients. The first data matrix contains two columns: one is the GenInfo Identifier (GI) numbers of the 16S rRNA gene sequences and the other is the corresponding oral bacterial taxonomy. The probes were then screened for redundancy and if they were found to be unique, they were synthesized onto the surface of the DNA microarrays. The second data matrix consists of the abundances of the 576 16S rRNA genes that was determined using the median value of all individual calibrated probes targeting each gene. The data matrix consists of 16 columns and 576 rows, with the columns representing the 16 patients and the rows representing 576 different oral microorganisms. The third data matrix consists of the abundances of 567 16S rRNA genes determined using the calibrated abundance of all aggregated probes targeting the same 16S rRNA gene. The data matrix of the aggregated probes consists of 16 samples and 567 rows.
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Project description:Currently, there is little evidence available on the development of predictive models for the diagnosis or prognosis of chronic periodontitis based on the qPCR quantification of subgingival pathobionts. Our objectives were to: (1) analyze and internally validate pathobiont-based models that could be used to distinguish different periodontal conditions at site-specific level within the same patient with chronic periodontitis; (2) develop nomograms derived from predictive models. Subgingival plaque samples were obtained from control and periodontal sites (probing pocket depth and clinical attachment loss <4 mm and >4 mm, respectively) from 40 patients with moderate-severe generalized chronic periodontitis. The samples were analyzed by qPCR using TaqMan probes and specific primers to determine the concentrations of Actinobacillus actinomycetemcomitans (Aa), Fusobacterium nucleatum (Fn), Parvimonas micra (Pm), Porphyromonas gingivalis (Pg), Prevotella intermedia (Pi), Tannerella forsythia (Tf), and Treponema denticola (Td). The pathobiont-based models were obtained using multivariate binary logistic regression. The best models were selected according to specified criteria. The discrimination was assessed using receiver operating characteristic curves and numerous classification measures were thus obtained. The nomograms were built based on the best predictive models. Eight bacterial cluster-based models showed an area under the curve (AUC) ?0.760 and a sensitivity and specificity ?75.0%. The PiTfFn cluster showed an AUC of 0.773 (sensitivity and specificity = 75.0%). When Pm and AaPm were incorporated in the TdPiTfFn cluster, we detected the two best predictive models with an AUC of 0.788 and 0.789, respectively (sensitivity and specificity = 77.5%). The TdPiTfAa cluster had an AUC of 0.785 (sensitivity and specificity = 75.0%). When Pm was incorporated in this cluster, a new predictive model appeared with better AUC and specificity values (0.787 and 80.0%, respectively). Distinct clusters formed by species with different etiopathogenic role (belonging to different Socransky's complexes) had a good predictive accuracy for distinguishing a site with periodontal destruction in a periodontal patient. The predictive clusters with the lowest number of bacteria were PiTfFn and TdPiTfAa, while TdPiTfAaFnPm had the highest number. In all the developed nomograms, high concentrations of these clusters were associated with an increased probability of having a periodontal site in a patient with chronic periodontitis.
| S-EPMC5552702 | biostudies-literature
Project description:Periodontitis is driven by disproportionate host inflammatory immune responses induced by an imbalance in the composition of oral bacteria; this instigates microbial dysbiosis, along with failed resolution of the chronic destructive inflammation. The objectives of this study were to identify microbial signatures for health and chronic periodontitis at the genus level and to propose a model of dysbiosis, including the calculation of bacterial ratios. Published sequencing data obtained from several different studies (196 subgingival samples from patients with chronic periodontitis and 422 subgingival samples from healthy subjects) were pooled and subjected to a new microbiota analysis using the same Visualization and Analysis of Microbial Population Structures (VAMPS) pipeline, to identify microbiota specific to health and disease. Microbiota were visualized using CoNet and Cytoscape. Dysbiosis ratios, defined as the percentage of genera associated with disease relative to the percentage of genera associated with health, were calculated to distinguish disease from health. Correlations between the proposed dysbiosis ratio and the periodontal pocket depth were tested with a different set of data obtained from a recent study, to confirm the relevance of the ratio as a potential indicator of dysbiosis. Beta diversity showed significant clustering of periodontitis-associated microbiota, at the genus level, according to the clinical status and independent of the methods used. Specific genera (Veillonella, Neisseria, Rothia, Corynebacterium, and Actinomyces) were highly prevalent (>95%) in health, while other genera (Eubacterium, Campylobacter, Treponema, and Tannerella) were associated with chronic periodontitis. The calculation of dysbiosis ratios based on the relative abundance of the genera found in health versus periodontitis was tested. Nonperiodontitis samples were significantly identifiable by low ratios, compared to chronic periodontitis samples. When applied to a subgingival sample set with well-defined clinical data, the method showed a strong correlation between the dysbiosis ratio, as well as a simplified ratio (Porphyromonas, Treponema, and Tannerella to Rothia and Corynebacterium), and pocket depth. Microbial analysis of chronic periodontitis can be correlated with the pocket depth through specific signatures for microbial dysbiosis.IMPORTANCE Defining microbiota typical of oral health or chronic periodontitis is difficult. The evaluation of periodontal disease is currently based on probing of the periodontal pocket. However, the status of pockets "on the mend" or sulci at risk of periodontitis cannot be addressed solely through pocket depth measurements or current microbiological tests available for practitioners. Thus, a more specific microbiological measure of dysbiosis could help in future diagnoses of periodontitis. In this work, data from different studies were pooled, to improve the accuracy of the results. However, analysis of multiple species from different studies intensified the bacterial network and complicated the search for reproducible microbial signatures. Despite the use of different methods in each study, investigation of the microbiota at the genus level showed that some genera were prevalent (up to 95% of the samples) in health or disease, allowing the calculation of bacterial ratios (i.e., dysbiosis ratios). The correlation between the proposed ratios and the periodontal pocket depth was tested, which confirmed the link between dysbiosis ratios and the severity of the disease. The results of this work are promising, but longitudinal studies will be required to improve the ratios and to define the microbial signatures of the disease, which will allow monitoring of periodontal pocket recovery and, conceivably, determination of the potential risk of periodontitis among healthy patients.
| S-EPMC5494626 | biostudies-literature
Project description:Periodontitis is a biofilm-induced inflammatory disease characterized by dysbiosis of the commensal periodontal microbiota. It is unclear how natural regulation of inflammation affects the periodontal biofilm. Promoters of active resolution of inflammation, including resolvin E1 (RvE1), effectively treat inflammatory periodontitis in animal models. The goals of this study were 1) to compare periodontal tissue gene expression in different clinical conditions, 2) to determine the impact of local inflammation on the composition of subgingival bacteria, and 3) to understand how inflammation impacts these changes. Two clinically relevant experiments were performed in rats: prevention and treatment of ligature-induced periodontitis with RvE1 topical treatment. The gingival transcriptome was evaluated by RNA sequencing of mRNA. The composition of the subgingival microbiota was characterized by 16S rDNA sequencing. Periodontitis was assessed by bone morphometric measurements and histomorphometry of block sections. H&E and tartrate-resistant acid phosphatase staining were used to characterize and quantify inflammatory changes. RvE1 treatment prevented bone loss in ligature-induced periodontitis. Osteoclast density and inflammatory cell infiltration in the RvE1 groups were lower than those in the placebo group. RvE1 treatment reduced expression of inflammation-related genes, returning the expression profile to one more similar to health. Treatment of established periodontitis with RvE1 reversed bone loss, reversed inflammatory gene expression, and reduced osteoclast density. Assessment of the rat subgingival microbiota after RvE1 treatment revealed marked changes in both prevention and treatment experiments. The data suggest that modulation of local inflammation has a major role in shaping the composition of the subgingival microbiota.
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