scholarly journals Oral Dysbiosis in Severe Forms of Periodontitis Is Associated With Gut Dysbiosis and Correlated With Salivary Inflammatory Mediators: A Preliminary Study

2021 ◽  
Vol 2 ◽  
Author(s):  
Dione Kawamoto ◽  
Rodrigo Borges ◽  
Rodolfo Alvarenga Ribeiro ◽  
Robson Franciso de Souza ◽  
Pâmela Pontes Penas Amado ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Inflammatory Mediator ◽  
Oral Microbiome ◽  
Sequencing Analysis ◽  
Fecal Microbiome ◽  
Haemophilus Parainfluenzae ◽  
Oral Biofilms ◽  
Inflammatory Chemokines ◽  
Preliminary Study ◽  
Microbiome Data

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.

scholarly journals G2S: A New Deep Learning Tool for Predicting Stool Microbiome Structure From Oral Microbiome Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Simone Rampelli ◽  
Marco Fabbrini ◽  
Marco Candela ◽  
Elena Biagi ◽  
Patrizia Brigidi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Microbiome ◽  
Oral Microbiome ◽  
Fecal Microbiome ◽  
The Family ◽  
Bioinformatic Tool ◽  
Microbial Metagenomics ◽  
Community Theory ◽  
Microbiome Data ◽  
Fecal Sampling

Deep learning methodologies have revolutionized prediction in many fields and show the potential to do the same in microbial metagenomics. However, deep learning is still unexplored in the field of microbiology, with only a few software designed to work with microbiome data. Within the meta-community theory, we foresee new perspectives for the development and application of deep learning algorithms in the field of the human microbiome. In this context, we developed G2S, a bioinformatic tool for taxonomic prediction of the human fecal microbiome directly from the oral microbiome data of the same individual. The tool uses a deep convolutional neural network trained on paired oral and fecal samples from populations across the globe, which allows inferring the stool microbiome at the family level more accurately than other available approaches. The tool can be used in retrospective studies, where fecal sampling was not performed, and especially in the field of paleomicrobiology, as a unique opportunity to recover data related to ancient gut microbiome configurations. G2S was validated on already characterized oral and fecal sample pairs, and then applied to ancient microbiome data from dental calculi, to derive putative intestinal components in medieval subjects.

scholarly journals Metagenomic and Metatranscriptomic Insight Into Oral Biofilms in Periodontitis and Related Systemic Diseases

2021 ◽  
Vol 12 ◽  
Author(s):  
Yi Huang ◽  
Xinyuan Zhao ◽  
Li Cui ◽  
Shaohong Huang
Keyword(s):  
Microbial Communities ◽  
Oral Microbiome ◽  
Etiologic Factor ◽  
Systemic Diseases ◽  
Pathogenic Microorganisms ◽  
Microbial Dysbiosis ◽  
Composition And Structure ◽  
Oral Biofilms ◽  
Systemic Health ◽  
Insight Into

The oral microbiome is one of the most complex microbial communities in the human body and is closely related to oral and systemic health. Dental plaque biofilms are the primary etiologic factor of periodontitis, which is a common chronic oral infectious disease. The interdependencies that exist among the resident microbiota constituents in dental biofilms and the interaction between pathogenic microorganisms and the host lead to the occurrence and progression of periodontitis. Therefore, accurately and comprehensively detecting periodontal organisms and dissecting their corresponding functional activity characteristics are crucial for revealing periodontitis pathogenesis. With the development of metagenomics and metatranscriptomics, the composition and structure of microbial communities as well as the overall functional characteristics of the flora can be fully profiled and revealed. In this review, we will critically examine the currently available metagenomic and metatranscriptomic evidence to bridge the gap between microbial dysbiosis and periodontitis and related systemic diseases.

scholarly journals Microbiome of Odontogenic Abscesses

2021 ◽  
Vol 9 (6) ◽  
pp. 1307
Author(s):  
Sebastian Böttger ◽  
Silke Zechel-Gran ◽  
Daniel Schmermund ◽  
Philipp Streckbein ◽  
Jan-Falco Wilbrand ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Oral Microbiome ◽  
Minor Role ◽  
Rrna Gene ◽  
Sequencing Analysis ◽  
Bacterial Strains ◽  
16S Rrna Gene Analysis ◽  
Odontogenic Infections ◽  
Odontogenic Abscess

Severe odontogenic abscesses are regularly caused by bacteria of the physiological oral microbiome. However, the culture of these bacteria is often prone to errors and sometimes does not result in any bacterial growth. Furthermore, various authors found completely different bacterial spectra in odontogenic abscesses. Experimental 16S rRNA gene next-generation sequencing analysis was used to identify the microbiome of the saliva and the pus in patients with a severe odontogenic infection. The microbiome of the saliva and the pus was determined for 50 patients with a severe odontogenic abscess. Perimandibular and submandibular abscesses were the most commonly observed diseases at 15 (30%) patients each. Polymicrobial infections were observed in 48 (96%) cases, while the picture of a mono-infection only occurred twice (4%). On average, 31.44 (±12.09) bacterial genera were detected in the pus and 41.32 (±9.00) in the saliva. In most cases, a predominantly anaerobic bacterial spectrum was found in the pus, while saliva showed a similar oral microbiome to healthy individuals. In the majority of cases, odontogenic infections are polymicrobial. Our results indicate that these are mainly caused by anaerobic bacterial strains and that aerobic and facultative anaerobe bacteria seem to play a more minor role than previously described by other authors. The 16S rRNA gene analysis detects significantly more bacteria than conventional methods and molecular methods should therefore become a part of routine diagnostics in medical microbiology.

scholarly journals Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Verónica Lloréns-Rico ◽  
Sara Vieira-Silva ◽  
Pedro J. Gonçalves ◽  
Gwen Falony ◽  
Jeroen Raes
Keyword(s):  
Microbial Communities ◽  
Quantitative Methods ◽  
Microbial Load ◽  
Metagenomic Sequencing ◽  
Sampling Depth ◽  
Microbiome Research ◽  
Microbiome Data ◽  
The Impact ◽  
Analytical Approaches ◽  
Quantitative Approaches

AbstractWhile metagenomic sequencing has become the tool of preference to study host-associated microbial communities, downstream analyses and clinical interpretation of microbiome data remains challenging due to the sparsity and compositionality of sequence matrices. Here, we evaluate both computational and experimental approaches proposed to mitigate the impact of these outstanding issues. Generating fecal metagenomes drawn from simulated microbial communities, we benchmark the performance of thirteen commonly used analytical approaches in terms of diversity estimation, identification of taxon-taxon associations, and assessment of taxon-metadata correlations under the challenge of varying microbial ecosystem loads. We find quantitative approaches including experimental procedures to incorporate microbial load variation in downstream analyses to perform significantly better than computational strategies designed to mitigate data compositionality and sparsity, not only improving the identification of true positive associations, but also reducing false positive detection. When analyzing simulated scenarios of low microbial load dysbiosis as observed in inflammatory pathologies, quantitative methods correcting for sampling depth show higher precision compared to uncorrected scaling. Overall, our findings advocate for a wider adoption of experimental quantitative approaches in microbiome research, yet also suggest preferred transformations for specific cases where determination of microbial load of samples is not feasible.

scholarly journals tidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Charlie M. Carpenter ◽  
Daniel N. Frank ◽  
Kayla Williamson ◽  
Jaron Arbet ◽  
Brandie D. Wagner ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Open Source ◽  
Data Structures ◽  
Negative Binomial ◽  
Rocky Mountain ◽  
R Package ◽  
Microbiome Analysis ◽  
External Data ◽  
Data Tables ◽  
Microbiome Data

Abstract Background The drive to understand how microbial communities interact with their environments has inspired innovations across many fields. The data generated from sequence-based analyses of microbial communities typically are of high dimensionality and can involve multiple data tables consisting of taxonomic or functional gene/pathway counts. Merging multiple high dimensional tables with study-related metadata can be challenging. Existing microbiome pipelines available in R have created their own data structures to manage this problem. However, these data structures may be unfamiliar to analysts new to microbiome data or R and do not allow for deviations from internal workflows. Existing analysis tools also focus primarily on community-level analyses and exploratory visualizations, as opposed to analyses of individual taxa. Results We developed the R package “tidyMicro” to serve as a more complete microbiome analysis pipeline. This open source software provides all of the essential tools available in other popular packages (e.g., management of sequence count tables, standard exploratory visualizations, and diversity inference tools) supplemented with multiple options for regression modelling (e.g., negative binomial, beta binomial, and/or rank based testing) and novel visualizations to improve interpretability (e.g., Rocky Mountain plots, longitudinal ordination plots). This comprehensive pipeline for microbiome analysis also maintains data structures familiar to R users to improve analysts’ control over workflow. A complete vignette is provided to aid new users in analysis workflow. Conclusions tidyMicro provides a reliable alternative to popular microbiome analysis packages in R. We provide standard tools as well as novel extensions on standard analyses to improve interpretability results while maintaining object malleability to encourage open source collaboration. The simple examples and full workflow from the package are reproducible and applicable to external data sets.

scholarly journals Metapangenomics of the oral microbiome provides insights into habitat adaptation and cultivar diversity

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Daniel R. Utter ◽  
Gary G. Borisy ◽  
A. Murat Eren ◽  
Colleen M. Cavanaugh ◽  
Jessica L. Mark Welch
Keyword(s):  
Buccal Mucosa ◽  
Habitat Preferences ◽  
Human Microbiome ◽  
Human Microbiome Project ◽  
Oral Microbiome ◽  
Functional Enrichment ◽  
Habitat Adaptation ◽  
Haemophilus Parainfluenzae ◽  
Population Structures ◽  
Genome Content

Abstract Background The increasing availability of microbial genomes and environmental shotgun metagenomes provides unprecedented access to the genomic differences within related bacteria. The human oral microbiome with its diverse habitats and abundant, relatively well-characterized microbial inhabitants presents an opportunity to investigate bacterial population structures at an ecosystem scale. Results Here, we employ a metapangenomic approach that combines public genomes with Human Microbiome Project (HMP) metagenomes to study the diversity of microbial residents of three oral habitats: tongue dorsum, buccal mucosa, and supragingival plaque. For two exemplar taxa, Haemophilus parainfluenzae and the genus Rothia, metapangenomes reveal distinct genomic groups based on shared genome content. H. parainfluenzae genomes separate into three distinct subgroups with differential abundance between oral habitats. Functional enrichment analyses identify an operon encoding oxaloacetate decarboxylase as diagnostic for the tongue-abundant subgroup. For the genus Rothia, grouping by shared genome content recapitulates species-level taxonomy and habitat preferences. However, while most R. mucilaginosa are restricted to the tongue as expected, two genomes represent a cryptic population of R. mucilaginosa in many buccal mucosa samples. For both H. parainfluenzae and the genus Rothia, we identify not only limitations in the ability of cultivated organisms to represent populations in their native environment, but also specifically which cultivar gene sequences are absent or ubiquitous. Conclusions Our findings provide insights into population structure and biogeography in the mouth and form specific hypotheses about habitat adaptation. These results illustrate the power of combining metagenomes and pangenomes to investigate the ecology and evolution of bacteria across analytical scales.

scholarly journals Energy-Efficient Single-Stage Nitrite Shunt Denitrification with Saline Sewage through Concise Dissolved Oxygen (DO) Supply: Process Performance and Microbial Communities

2020 ◽  
Vol 8 (6) ◽  
pp. 919 ◽  
Author(s):  
Huichuan Zhuang ◽  
Zhuoying Wu ◽  
Linji Xu ◽  
Shao-Yuan Leu ◽  
Po-Heng Lee
Keyword(s):  
Dissolved Oxygen ◽  
Microbial Communities ◽  
Energy Efficient ◽  
Oxygen Demand ◽  
Operating Conditions ◽  
Single Stage ◽  
Rrna Gene ◽  
Unexpected Finding ◽  
Sequencing Analysis ◽  
Ratio Requirement

Single-stage nitrite shunt denitrification (through nitrite rather than nitrate) with low dissolved oxygen (DO) supply is a better alternative in terms of energy-efficiency, short-footprint, and low C/N-ratio requirement. This study investigates the optimal DO level with temperature effect, with saline sewage at the fixed hydraulic and solids retention times of 8 h and 8 d, respectively. Moreover, 16S rRNA gene sequencing analysis corresponding with total nitrogen (TN) and chemical oxygen demand (COD) removals in each operating condition were performed. Results showed that DO of 0.3 mg/L at 20 °C achieved over 60.7% and over 97.9% of TN and COD removal, respectively, suggesting that such condition achieved effective nitrite-oxidizing bacteria inhibition and efficient denitrification. An unexpected finding was that sulfur-reducing Haematobacter and nitrogen-fixing Geofilum and Shinella were highly abundant with the copredominance of ammonia-oxidizing Comamonas and Nitrosomonas, nitrite-oxidizing Limnohabitans, and denitrifying Simplicispira, Castellaniella, and Nitratireductor. Further, canonical correspondence analysis (CCA) with respect to the operating conditions associated with phenotype prediction via R-based tool Tax4Fun was performed for a preliminary diagnosis of microbial functionality. The effects of DO, temperature, nitrite, and nitrate in various extents toward each predominant microbe were discussed. Collectively, DO is likely pivotal in single-stage nitrite shunt denitrification, as well as microbial communities, for energy-efficient saline sewage treatment.

scholarly journals Antimicrobial and Substantivity Properties of Silver Nanoparticles against Oral Microbiomes Clinically Isolated from Young and Young-Adult Patients

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
León Francisco Espinosa-Cristóbal ◽  
Carolina Holguín-Meráz ◽  
Erasto Armando Zaragoza-Contreras ◽  
Rita Elizabeth Martínez-Martínez ◽  
Alejandro Donohue-Cornejo ◽  
...  
Keyword(s):  
Silver Nanoparticles ◽  
Young Adult ◽  
Dental Caries ◽  
Periodontal Disease ◽  
Dental Plaque ◽  
Bacterial Species ◽  
Inhibitory Effect ◽  
Oral Bacteria ◽  
Oral Microbiome ◽  
Oral Biofilms

The dental plaque is an oral microbiome hardly associated to be the etiological agent of dental caries and periodontal disease which are still considered serious health public problems. Silver nanoparticles (AgNPs) have demonstrated to have good antimicrobial properties affecting a wide variety of microorganisms, including oral bacteria; however, there is no scientific information that has evaluated the antimicrobial effect of AgNPs against clinical oral biofilms associated with dental caries and periodontal disease. The aim of this study was to determine the antimicrobial and substantivity effects of AgNPs in oral biofilms isolated clinically from patients with dental caries and periodontal disease. Sixty-seven young and young-adult subjects with dental caries and periodontal disease were clinically sampled through the collection of subgingival dental plaque. The inhibitory effect of AgNPs was performed with standard microbiological assays by triplicate using two sizes of particle. Polymerase chain reaction (PCR) assay was used to identify the presence of specific bacterial species. All AgNPs showed an inhibitory effect for all oral biofilms for any age and, generally, any gender (p>0.05); however, the effectiveness of the antimicrobial and substantivity effects was related to particle size, time, and gender (p<0.05). The identified microorganisms were S. mutans, S. sobrinus, S. sanguinis, S. gordonii, S. oralis, P. gingivalis, T. forsythia, and P. intermedia. The AgNPs could be considered as a potential antimicrobial agent for the control and prevention of dental caries and periodontal disease.

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