Toward Personalized Oral Diagnosis: Distinct Microbiome Clusters in Periodontitis Biofilms.
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ABSTRACT:
SUBMITTER: Wirth R
PROVIDER: S-EPMC8727345 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:HIV-associated periodontal diseases (PD) could serve as a source of chronic inflammation. Here, we sought to characterize the oral microbial signatures of HIV+ and HIV- individuals at different levels of PD severity.This cross-sectional study included both HIV+ and HIV- patients with varying degrees of PD. Two tooth, 2 cheek, and 1 saliva samples were obtained for microbiome analysis. Mothur/SILVADB were used to classify sequences. R/Bioconductor (Vegan, PhyloSeq, and DESeq2) was employed to assess overall microbiome structure differences and differential abundance of bacterial genera between groups. Polychromatic flow cytometry was used to assess immune activation in CD4 and CD8 cell populations.Around 250 cheek, tooth, and saliva samples from 50 participants (40 HIV+ and 10 HIV-) were included. Severity of PD was classified clinically as None/Mild (N), Moderate (M), and Severe (S) with 18 (36%), 16 (32%), and 16 (32%) participants in each category, respectively. Globally, ordination analysis demonstrated clustering by anatomic site (R2?=?0.25, P?<?0.001). HIV status and PD severity showed a statistically significant impact on microbiome composition but only accounted for a combined 2% of variation. HIV+ samples were enriched in genera Abiotrophia, Neisseria, Kingella, and unclassified Neisseriaceae and depleted in Leptotrichia and Selenomonas. The Neisseria genus was consistently enriched in HIV+ participants regardless of sampling site and PD level. Immune markers were altered in HIV+ participants but did not show association with the oral microbiome.HIV-associated changes in oral microbiome result in subtle microbial signatures along different stages of PD that are common in independent oral anatomic sites.
| S-EPMC5371436 | biostudies-literature
Project description:Objective: Periodontal diseases are triggered by dysbiotic microbial biofilms. Therefore, it is essential to develop appropriate biofilm models. Aim of the present study was to culture microcosm biofilms inoculated from different niches in periodontitis patients and compare their microbial composition to those inoculated from subgingival plaque. Methods: Saliva, subgingival plaque, tongue and tonsils were sampled in five periodontitis patients to serve as inocula for culturing biofilms in vitro in an active attachment model. Biofilms were grown for 14 or 28 d and analyzed for their microbial composition by 16S rDNA sequencing. Results: As classified by HOMD, all biofilms were dominated by periodontitis-associated taxa, irrespective which niche had been used for inoculation. There was a low similarity between 14 d biofilms and their respective inocula (Bray-Curtis similarity 0.26), while biofilms cultured for 14 and 28 d shared high similarity (0.69). Principal components analysis showed much stronger clustering per patient than per niche indicating that the choice of patients may be more crucial than choice of the respective niches in these patients. Conclusion: Saliva, tongue scrapings or tonsil swabs may represent sufficient alternative inocula for growing microcosm biofilms resembling periodontitis-associated microbial communities in cases when sampling subgingival plaque is not possible.
| S-EPMC6263112 | biostudies-literature
Project description:In order to improve our understanding on the microbial complexity associated with Grade C/molar-incisor pattern periodontitis (GC/MIP), we surveyed the oral and fecal microbiomes of GC/MIP and compared to non-affected individuals (Control). Seven Afro-descendants with GC/MIP and seven age/race/gender-matched controls were evaluated. Biofilms from supra/subgingival sites (OB) and feces were collected and submitted to 16S rRNA sequencing. Aggregatibacter actinomycetemcomitans (Aa) JP2 clone genotyping and salivary nitrite levels were determined. Supragingival biofilm of GC/MIP presented greater abundance of opportunistic bacteria. Selenomonas was increased in subgingival healthy sites of GC/MIP compared to Control. Synergistetes and Spirochaetae were more abundant whereas Actinobacteria was reduced in OB of GC/MIP compared to controls. Aa abundance was 50 times higher in periodontal sites with PD? 4 mm of GC/MIP than in controls. GC/MIP oral microbiome was characterized by a reduction in commensals such as Kingella, Granulicatella, Haemophilus, Bergeyella, and Streptococcus and enrichment in periodontopathogens, especially Aa and sulfate reducing Deltaproteobacteria. The oral microbiome of the Aa JP2-like+ patient was phylogenetically distant from other GC/MIP individuals. GC/MIP presented a higher abundance of sulfidogenic bacteria in the feces, such as Desulfovibrio fairfieldensis, Erysipelothrix tonsillarum, and Peptostreptococcus anaerobius than controls. These preliminary data show that the dysbiosis of the microbiome in Afro-descendants with GC/MIP was not restricted to affected sites, but was also observed in supragingival and subgingival healthy sites, as well as in the feces. The understanding on differences of the microbiome between healthy and GC/MIP patients will help in developing strategies to improve and monitor periodontal treatment.
| S-EPMC7578221 | biostudies-literature
Project description:Metagenomic shotgun sequencing of oral samples of study participants with different severity of periodontitis. Sampling includes two locations, saliva and subgingival pocket samples.
| PRJEB42701 | ENA
Project description:Dysbiosis of the oral microbiome is associated with diseases such as periodontitis and dental caries. Because the bacterial counts in saliva increase markedly during sleep, it is broadly accepted that the mouth should be cleaned before sleep to help prevent these diseases. However, this practice does not consider oral biofilms, including the dental biofilm. This study aimed to investigate sleep-related changes in the microbiome of oral biofilms by using 16S rRNA gene sequence analysis. Two experimental schedules-post-sleep and pre-sleep biofilm collection-were applied to 10 healthy subjects. Subjects had their teeth and oral mucosa professionally cleaned 7 days and 24 h before sample collection. Samples were collected from several locations in the oral cavity: the buccal mucosa, hard palate, tongue dorsum, gingival mucosa, tooth surface, and saliva. Prevotella and Corynebacterium had higher relative abundance on awakening than before sleep in all locations of the oral cavity, whereas fluctuations in Rothia levels differed depending on location. The microbiome in different locations in the oral cavity is affected by sleep, and changes in the microbiome composition depend on characteristics of the surfaces on which oral biofilms form.
| S-EPMC8659294 | biostudies-literature
Project description:Systemic inflammation induced by periodontitis is suggested to be the link between periodontitis and cardiovascular disease. The aim of this work was to explore the oral microbiome in periodontitis in relation to disease severity and systemic inflammation. The saliva and subgingival microbiome from periodontal pocket samples of patients with severe (n = 12) and mild periodontitis (n = 13) were analyzed using metagenomic shotgun sequencing. The taxa and pathways abundances were quantified. The diversity was assessed and the abundances to phenotype associations were performed using ANCOM and linear regression. A panel of inflammatory markers was measured in blood and was associated with taxa abundance. The microbial diversity and species richness did not differ between severe and mild periodontitis in either saliva or periodontal pockets. However, there were significant differences in the microbial composition between severe and mild periodontitis in the subgingival microbiome (i.e., pocket samples) and, in a lower grade, in saliva, and this is positively associated with systemic inflammatory markers. The "red complex" and "cluster B" abundances in periodontal pockets were strongly associated with inflammatory markers interleukin-6 and the white blood cell count. Our data suggest that systemic inflammation in severe periodontitis may be driven by the oral microbiome and may support the indirect (inflammatory) mechanism for the association between periodontitis and cardiovascular disease.
| S-EPMC8199296 | biostudies-literature
Project description:The oral microbiome is one of the most complex microbial communities in the human body, and due to circumstances not completely understood, the healthy microbial community becomes dysbiotic, giving rise to periodontitis, a polymicrobial inflammatory disease. We previously reported the results of community-wide gene expression changes in the oral microbiome during periodontitis progression and identified signatures associated with increasing severity of the disease. Small noncoding RNAs (sRNAs) are key players in posttranscriptional regulation, especially in fast-changing environments such as the oral cavity. Here, we expanded our analysis to the study of the sRNA metatranscriptome during periodontitis progression on the same samples for which mRNA expression changes were analyzed. We observed differential expression of 12,097 sRNAs, identifying a total of 20 Rfam sRNA families as being overrepresented in progression and 23 at baseline. Gene ontology activities regulated by the differentially expressed (DE) sRNAs included amino acid metabolism, ethanolamine catabolism, signal recognition particle-dependent cotranslational protein targeting to membrane, intron splicing, carbohydrate metabolism, control of plasmid copy number, and response to stress. In integrating patterns of expression of protein coding transcripts and sRNAs, we found that functional activities of genes that correlated positively with profiles of expression of DE sRNAs were involved in pathogenesis, proteolysis, ferrous iron transport, and oligopeptide transport. These findings represent the first integrated sequencing analysis of the community-wide sRNA transcriptome of the oral microbiome during periodontitis progression and show that sRNAs are key regulatory elements of the dysbiotic process leading to disease.
| S-EPMC4561685 | biostudies-literature
Project description:This investigation evaluated the relationship of the oral microbiome and gingival transcriptome in health and periodontitis in nonhuman primates (Macaca mulatta). Subgingival plaque samples and gingival biopsies were collected from healthy sites and at sites undergoing ligature-induced periodontitis. Microbial samples were analyzed with 16S amplicon sequencing to identify bacterial profiles in young (3 to 7 y) and adult (12 to 23 y) animals. The gingival transcriptome was determined with a microarray analysis and focused on the expression level of 452 genes that are associated with the development of inflammation and innate and adaptive immune responses. Of the 396 total operational taxonomic units (OTUs) identified across the samples, 81.8% were detected in the young group and 99.5% in the adult group. Nevertheless, 58 of the OTUs composed 88% of the signal in adults, and 49 OTUs covered 91% of the OTU readouts in the young group. Correlation analyses between the microbiome members and specific gingival genes showed a high number of significant bacteria-gene correlations in the young healthy tissues, which decreased by 75% in diseased tissues. In contrast, these correlations increased by 2.5-fold in diseased versus healthy tissues of adult animals. Complexes of bacteria were delineated that related to specific sets of immune genes, differing in health and disease and in the young versus adult animals. The correlated gene profiles demonstrated selected pathway overrepresentation related to particular bacterial complexes. These results provide novel insights into microbiome changes with disease and the relationship of these changes to specific gene profiles and likely biologic activities occurring in healthy and diseased gingival tissues in this human-like periodontitis model.
| S-EPMC7243415 | biostudies-literature
Project description:BackgroundSequencing of the 16S rRNA gene has been the standard for studying the composition of microbial communities. While it allows identification of bacteria at the level of species, this method does not usually provide sufficient information to resolve communities at the sub-species level. Species-level resolution is not adequate for studies of transmission or stability or for exploring subspecies variation in disease association. Strain level analysis using whole metagenome shotgun sequencing has significant limitations that can make it unsuitable for large-scale studies. Achieving sufficient depth of sequencing can be cost-prohibitive, and even with adequate coverage, deconvoluting complex communities such as the oral microbiota is computationally very challenging. Thus, there is a need for high-resolution, yet cost-effective, high-throughput methods for characterizing microbial communities.ResultsSignificant improvement in resolution for amplicon-based bacterial community analysis was achieved by combining amplicon sequencing of a high-diversity marker gene, the ribosomal 16-23S intergenic spacer region (ISR), with a probabilistic error modeling based denoising algorithm, DADA2. The resolving power of this new approach was compared to that of both standard and high-resolution 16S-based approaches using a set of longitudinal subgingival plaque samples. The ISR strategy resulted in a 5.2-fold increase in community resolution compared to reference-based 16S rRNA gene analysis and showed 100% accuracy in predicting the correct source of a clinical sample. Individuals' microbial communities were highly personalized, and although they exhibited some drift in membership and levels over time, that difference was always smaller than the differences between any two subjects, even after 1 year. The construction of an ISR database from publicly available genomic sequences allowed us to explore genomic variation within species, resulting in the identification of multiple variants of the ISR for most species.ConclusionsThe ISR approach resulted in significantly improved resolution of communities and revealed a highly personalized human oral microbiota that was stable over 1 year. Multiple ISR types were observed for all species examined, demonstrating a high level of subspecies variation in the oral microbiota. The approach is high-throughput, high-resolution yet cost-effective, allowing subspecies-level community fingerprinting at a cost comparable to that of 16S rRNA gene amplicon sequencing. It will be useful for a range of applications that require high-resolution identification of organisms, including microbial tracking, community fingerprinting, and potentially for identification of virulence-associated strains.
| S-EPMC6126016 | biostudies-literature
Project description:The problem of creating a personalized seizure detection algorithm for newborns is tackled in this paper. A probabilistic framework for semi-supervised adaptation of a generic patient-independent neonatal seizure detector is proposed. A system that is based on a combination of patient-adaptive (generative) and patient-independent (discriminative) classifiers is designed and evaluated on a large database of unedited continuous multichannel neonatal EEG recordings of over 800 h in duration. It is shown that an improvement in the detection of neonatal seizures over the course of long EEG recordings is achievable with on-the-fly incorporation of patient-specific EEG characteristics. In the clinical setting, the employment of the developed system will maintain a seizure detection rate at 70% while halving the number of false detections per hour, from 0.4 to 0.2 FD/h. This is the first study to propose the use of online adaptation without clinical labels, to build a personalized diagnostic system for the detection of neonatal seizures.
| S-EPMC5633333 | biostudies-literature