The bacteriome of the oral cavity in healthy dogs and dogs with periodontal disease

2022 ◽  
pp. 1-9
Author(s):  
Brook A. Niemiec ◽  
Jerzy Gawor ◽  
Shuiquan Tang ◽  
Aishani Prem ◽  
Janina A. Krumbeck
Keyword(s):  
Oral Cavity ◽  
Periodontal Disease ◽  
Bacterial Species ◽  
Abundant Species ◽  
Rrna Gene ◽  
Core Microbiome ◽  
The Core ◽  
Severe Periodontal Disease ◽  
Healthy Dogs ◽  
V3 Region

Abstract OBJECTIVE To compare the bacteriome of the oral cavity in healthy dogs and dogs with various stages of periodontal disease. ANIMALS Dogs without periodontal disease (n = 12) or with mild (10), moderate (19), or severe (10) periodontal disease. PROCEDURES The maxillary arcade of each dog was sampled with a sterile swab, and swabs were submitted for next-generation DNA sequencing targeting the V1–V3 region of the 16S rRNA gene. RESULTS 714 bacterial species from 177 families were identified. The 3 most frequently found bacterial species were Actinomyces sp (48/51 samples), Porphyromonas cangingivalis (47/51 samples), and a Campylobacter sp (48/51 samples). The most abundant species were P cangingivalis, Porphyromonas gulae, and an undefined Porphyromonas sp. Porphyromonas cangingivalis and Campylobacter sp were part of the core microbiome shared among the 4 groups, and P gulae, which was significantly enriched in dogs with severe periodontal disease, was part of the core microbiome shared between all groups except dogs without periodontal disease. Christensenellaceae sp, Bacteroidales sp, Family XIII sp, Methanobrevibacter oralis, Peptostreptococcus canis, and Tannerella sp formed a unique core microbiome in dogs with severe periodontal disease. CONCLUSIONS AND CLINICAL RELEVANCE Results highlighted that in dogs, potential pathogens can be common members of the oral cavity bacteriome in the absence of disease, and changes in the relative abundance of certain members of the bacteriome can be associated with severity of periodontal disease. Future studies may aim to determine whether these changes are the cause or result of periodontal disease or the host immune response.

scholarly journals The Subgingival Microbiome Relationship to Periodontal Disease in Older Women

2019 ◽  
Vol 98 (9) ◽  
pp. 975-984 ◽  
Author(s):  
R.J. Genco ◽  
M.J. LaMonte ◽  
D.I. McSkimming ◽  
M.J. Buck ◽  
L. Li ◽  
...  
Keyword(s):  
16S Rrna ◽  
Periodontal Disease ◽  
Older Women ◽  
Bacterial Species ◽  
Disease Status ◽  
Oral Microbiome ◽  
Rrna Gene ◽  
Minimum Entropy ◽  
Pocket Depth ◽  
Severe Periodontal Disease

Understanding of the oral microbiome in relation to periodontal disease in older adults is limited. The composition and diversity of the subgingival microflora and their oligotypes in health and levels of periodontal disease were investigated in this study on older postmenopausal women. The 16S rRNA gene was sequenced using the Illumina MiSeq platform in 1,206 women aged 53 to 81 y. Presence and severity of periodontal disease were defined by Centers for Disease Control and Prevention/American Academy of Periodontology criteria. Composition of the microbiome was determined by 16S rRNA amplicon sequencing and the abundance of taxa described by the centered log2-ratio (CLR) transformed operational taxonomic unit (OTU) values. Differences according to periodontal disease status were determined by analysis of variance with Bonferroni correction. Bacteria oligotypes associated with periodontal disease and health were determined by minimum entropy decomposition and their functions estimated in silico using PICRUSt. Prevalence of none/mild, moderate, and severe periodontal disease was 25.1%, 58.3%, and 16.6%, respectively. Alpha diversity of the microbiome differed significantly across the 3 periodontal disease categories. β-Diversity differed between no/mild and severe periodontal disease, although considerable overlap was noted. Of the 267 bacterial species identified at ≥0.02% abundance, 56 (20.9%) differed significantly in abundance according to periodontal disease status. Significant linear correlations for pocket depth and clinical attachment level with bacterial amounts were observed for several taxa. Of the taxa differing in abundance according to periodontal disease status, 53% had multiple oligotypes appearing to differ between none/mild and severe periodontal disease. Among older women, taxonomic differences in subgingival microbiome composition and diversity were observed in relation to clinical periodontal disease measures. Potential differences in bacterial subspecies (oligotypes) and their function were also identified in periodontal disease compared with health.

scholarly journals Relationship between dental and periodontal health status and the salivary microbiome: bacterial diversity, co-occurrence networks and predictive models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
M. Relvas ◽  
A. Regueira-Iglesias ◽  
C. Balsa-Castro ◽  
F. Salazar ◽  
J. J. Pacheco ◽  
...  
Keyword(s):  
Oral Health ◽  
Periodontal Disease ◽  
Bacterial Diversity ◽  
Predictive Models ◽  
Oral Disease ◽  
Rrna Gene ◽  
Convenience Sample ◽  
Core Microbiome ◽  
Rdna Sequencing ◽  
The Impact

AbstractThe present study used 16S rRNA gene amplicon sequencing to assess the impact on salivary microbiome of different grades of dental and periodontal disease and the combination of both (hereinafter referred to as oral disease), in terms of bacterial diversity, co-occurrence network patterns and predictive models. Our scale of overall oral health was used to produce a convenience sample of 81 patients from 270 who were initially recruited. Saliva samples were collected from each participant. Sequencing was performed in Illumina MiSeq with 2 × 300 bp reads, while the raw reads were processed according to the Mothur pipeline. The statistical analysis of the 16S rDNA sequencing data at the species level was conducted using the phyloseq, DESeq2, Microbiome, SpiecEasi, igraph, MixOmics packages. The simultaneous presence of dental and periodontal pathology has a potentiating effect on the richness and diversity of the salivary microbiota. The structure of the bacterial community in oral health differs from that present in dental, periodontal or oral disease, especially in high grades. Supragingival dental parameters influence the microbiota’s abundance more than subgingival periodontal parameters, with the former making a greater contribution to the impact that oral health has on the salivary microbiome. The possible keystone OTUs are different in the oral health and disease, and even these vary between dental and periodontal disease: half of them belongs to the core microbiome and are independent of the abundance parameters. The salivary microbiome, involving a considerable number of OTUs, shows an excellent discriminatory potential for distinguishing different grades of dental, periodontal or oral disease; considering the number of predictive OTUs, the best model is that which predicts the combined dental and periodontal status.

scholarly journals Uncovering the Core Microbiome and Distribution of Palmerolide in Synoicum adareanum Across the Anvers Island Archipelago, Antarctica

Marine Drugs ◽  
2020 ◽  
Vol 18 (6) ◽  
pp. 298
Author(s):  
Alison E. Murray ◽  
Nicole E. Avalon ◽  
Lucas Bishop ◽  
Karen W. Davenport ◽  
Erwan Delage ◽  
...  
Keyword(s):  
Marine Invertebrate ◽  
Bioactive Compound ◽  
Rrna Gene ◽  
Sequence Variants ◽  
Specific Sequence ◽  
Core Microbiome ◽  
Microbiome Composition ◽  
The Core ◽  
Synoicum Adareanum ◽  
Palmerolide A

Polar marine ecosystems hold the potential for bioactive compound biodiscovery, based on their untapped macro- and microorganism diversity. Characterization of polar benthic marine invertebrate-associated microbiomes is limited to few studies. This study was motivated by our interest in better understanding the microbiome structure and composition of the ascidian, Synoicum adareanum, in which palmerolide A (PalA), a bioactive macrolide with specificity against melanoma, was isolated. PalA bears structural resemblance to a hybrid nonribosomal peptide-polyketide that has similarities to microbially-produced macrolides. We conducted a spatial survey to assess both PalA levels and microbiome composition in S. adareanum in a region of the Antarctic Peninsula near Anvers Island (64°46′ S, 64°03′ W). PalA was ubiquitous and abundant across a collection of 21 ascidians (3 subsamples each) sampled from seven sites across the Anvers Island Archipelago. The microbiome composition (V3–V4 16S rRNA gene sequence variants) of these 63 samples revealed a core suite of 21 bacterial amplicon sequence variants (ASVs)—20 of which were distinct from regional bacterioplankton. ASV co-occurrence analysis across all 63 samples yielded subgroups of taxa that may be interacting biologically (interacting subsystems) and, although the levels of PalA detected were not found to correlate with specific sequence variants, the core members appeared to occur in a preferred optimum and tolerance range of PalA levels. These results, together with an analysis of the biosynthetic potential of related microbiome taxa, describe a conserved, high-latitude core microbiome with unique composition and substantial promise for natural product biosynthesis that likely influences the ecology of the holobiont.

The mycobiome of the oral cavity in healthy dogs and dogs with periodontal disease

2022 ◽  
pp. 1-8
Author(s):  
Brook A. Niemiec ◽  
Jerzy Gawor ◽  
Shuiquan Tang ◽  
Aishani Prem ◽  
Janina A. Krumbeck
Keyword(s):  
Oral Cavity ◽  
Periodontal Disease ◽  
Dna Sequencing ◽  
Fungal Species ◽  
Next Generation ◽  
Data Set ◽  
Next Generation Dna Sequencing ◽  
Sequencing Platform ◽  
The Family ◽  
Healthy Dogs

Abstract OBJECTIVE To investigate the mycobiome of the oral cavity in healthy dogs and dogs with various stages of periodontal disease. ANIMALS 51 dogs without periodontal disease (n = 12) or with mild (10), moderate (19), or severe (10) periodontal disease. PROCEDURES The whole maxillary arcade of each dog was sampled with a sterile swab, and swabs were submitted for next-generation DNA sequencing targeting the internal transcribed spacer 2 region with a commercial sequencing platform. RESULTS Fungi were detected in all samples, with a total of 320 fungal species from 135 families detected in the data set. No single fungal species was found in all samples. The 3 most frequently found fungal species were Cladosporium sp (46/51 samples), Malassezia restricta (44/51 samples), and Malassezia arunalokei (36/51 samples). Certain fungi, specifically those of the family Didymellaceae, the family Irpicaceae, and the order Pleosporales, were significantly associated with different stages of periodontitis. Mycobial analysis indicated that Cladosporium sp could be considered part of the core oral cavity mycobiome. CONCLUSIONS AND CLINICAL RELEVANCE Results highlighted that fungi are present in the oral cavity of dogs and are characterized by substantial species diversity, with different fungal communities associated with various stages of periodontal disease. The next-generation DNA sequencing used in the present study revealed substantially more species of fungi than previous culture-based studies.

scholarly journals Microbial Diversity Profiling of Gut Microbiota of Macropus giganteus Using Three Hypervariable Regions of the Bacterial 16S rRNA

2021 ◽  
Vol 9 (8) ◽  
pp. 1721
Author(s):  
Christian O’Dea ◽  
Roger Huerlimann ◽  
Nicole Masters ◽  
Anna Kuballa ◽  
Cameron Veal ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
Microbial Diversity ◽  
Surface Waters ◽  
Bacterial Species ◽  
Rrna Gene ◽  
Faecal Contamination ◽  
Hypervariable Regions ◽  
Geographical Regions ◽  
V3 Region

Animal faecal contamination of surface waters poses a human health risk, as they may contain pathogenic bacteria or viruses. Of the numerous animal species residing along surface waterways in Australia, macropod species are a top contributor to wild animals’ faecal pollution load. We characterised the gut microbiota of 30 native Australian Eastern Grey Kangaroos from six geographical regions (five kangaroos from each region) within South East Queensland in order to establish their bacterial diversity and identify potential novel species-specific bacteria for the rapid detection of faecal contamination of surface waters by these animals. Using three hypervariable regions (HVRs) of the 16S rRNA gene (i.e., V1–V3, V3–V4, and V5–V6), for their effectiveness in delineating the gut microbial diversity, faecal samples from each region were pooled and microbial genomic DNA was extracted, sequenced, and analysed. Results indicated that V1-V3 yielded a higher taxa richness due to its larger target region (~480 bp); however, higher levels of unassigned taxa were observed using the V1-V3 region. In contrast, the V3–V4 HVR (~569 bp) attained a higher likelihood of a taxonomic hit identity to the bacterial species level, with a 5-fold decrease in unassigned taxa. There were distinct dissimilarities in beta diversity between the regions, with the V1-V3 region displaying the highest number of unique taxa (n = 42), followed by V3–V4 (n = 11) and V5–V6 (n = 8). Variations in the gut microbial diversity profiles of kangaroos from different regions were also observed, which indicates that environmental factors may impact the microbial development and, thus, the composition of the gut microbiome of these animals.

scholarly journals Transition of Bacterial Diversity and Composition in Tongue Microbiota during the First Two Years of Life

mSphere ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Shinya Kageyama ◽  
Mikari Asakawa ◽  
Toru Takeshita ◽  
Yukari Ihara ◽  
Shunsuke Kanno ◽  
...  
Keyword(s):  
Oral Cavity ◽  
Bacterial Diversity ◽  
Bacterial Species ◽  
16S Rrna Gene Sequencing ◽  
Rrna Gene ◽  
Oral Microbiota ◽  
Bacterial Composition ◽  
Content Type ◽  
Operational Taxonomic Units ◽  
Rrna Gene Sequencing

ABSTRACTNewborns are constantly exposed to various microbes from birth; hence, diverse commensal bacteria colonize the oral cavity. However, how or when these bacteria construct a complex and stable ecosystem remains unclear. This prospective cohort study examined the temporal changes in bacterial diversity and composition in tongue microbiota during infancy. We longitudinally collected a total of 464 tongue swab samples from 8 infants (age of <6 months at baseline) for approximately 2 years. We also collected samples from 32 children (aged 0 to 2 years) and 73 adults (aged 20 to 29 years) cross-sectionally as control groups. Bacterial diversities and compositions were determined by 16S rRNA gene sequencing. The tongue bacterial diversity in infancy, measured as the number of observed operational taxonomic units (OTUs), rapidly increased and nearly reached the same level as that in adults by around 80 weeks. The overall tongue bacterial composition in the transitional phase, 80 to 120 weeks, was more similar to that of adults than to that of the early exponential phase (EEP), 10 to 29 weeks, according to analysis of similarities. Dominant OTUs in the EEP corresponding toStreptococcus perorisandStreptococcus lactariusexponentially decreased immediately after EEP, around 30 to 49 weeks, whereas several OTUs corresponding toGranulicatella adiacens,Actinomyces odontolyticus, andFusobacterium periodonticumreciprocally increased during the same period. These results suggest that a drastic compositional shift of tongue microbiota occurs before the age of 1 year, and then bacterial diversity and overall bacterial composition reach levels comparable to those in adults by the age of 2 years.IMPORTANCEEvaluating the development of oral microbiota during infancy is important for understanding the subsequent colonization of bacterial species and the process of formation of mature microbiota in the oral cavity. We examined tongue microbiota longitudinally collected from 8 infants and found that drastic compositional shifts in tongue microbiota occur before the age of 1 year, and then bacterial diversity and overall bacterial composition reach levels comparable to those in adults by the age of 2 years. These results may be helpful for preventing the development of various diseases associated with oral microbiota throughout life.

Comparative analysis of microbiome between accurately identified 16S rDNA and quantified bacteria in simulated samples

2014 ◽  
Vol 63 (3) ◽  
pp. 433-440 ◽  
Author(s):  
Haiyin Wang ◽  
Pengcheng Du ◽  
Juan Li ◽  
Yuanyuan Zhang ◽  
Wen Zhang ◽  
...  
Keyword(s):  
Microbial Communities ◽  
16S Rdna ◽  
Gold Standard ◽  
Clinical Utility ◽  
Bacterial Species ◽  
Relative Proportion ◽  
Rrna Gene ◽  
Low Concentrations ◽  
Rdna Sequencing ◽  
V3 Region

Although 16S rRNA gene (rDNA) sequencing is the gold standard for categorizing bacteria or characterizing microbial communities its clinical utility is limited by bias in metagenomic studies, in either the experiments or the data analyses. To evaluate the efficiency of current metagenomic methods, we sequenced seven simulated samples of ten bacterial species mixed at different concentrations. The V3 region of 16S rDNA was targeted and used to determine the distribution of bacterial species. The number of target sequences in individual simulated samples was in the range 1–1000 to provide a better reflection of natural microbial communities. However, for a given bacterial species present in the same proportion but at different concentrations, the observed percentage of 16S rDNAs was similar, except at very low concentrations that cannot be detected by real-time PCR. These results confirmed that the comparative microbiome in a sample characterized by 16S rDNA sequencing is sufficient to detect not only potential infectious pathogens, but also the relative proportion of 16S rDNA in the sample.

scholarly journals Metaproteomics Analysis Reveals the Adaptation Process for the Chicken Gut Microbiota

2013 ◽  
Vol 80 (2) ◽  
pp. 478-485 ◽  
Author(s):  
Yue Tang ◽  
Anthony Underwood ◽  
Adriana Gielbert ◽  
Martin J. Woodward ◽  
Liljana Petrovska
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
High Throughput Sequencing ◽  
Bacterial Species ◽  
Abundant Species ◽  
Fecal Microbiota ◽  
Rrna Gene ◽  
Sequencing Analysis ◽  
Content Type ◽  
Chicken Gut Microbiota

ABSTRACTThe animal gastrointestinal tract houses a large microbial community, the gut microbiota, that confers many benefits to its host, such as protection from pathogens and provision of essential metabolites. Metagenomic approaches have defined the chicken fecal microbiota in other studies, but here, we wished to assess the correlation between the metagenome and the bacterial proteome in order to better understand the healthy chicken gut microbiota. Here, we performed high-throughput sequencing of 16S rRNA gene amplicons and metaproteomics analysis of fecal samples to determine microbial gut composition and protein expression. 16 rRNA gene sequencing analysis identifiedClostridiales,Bacteroidaceae, andLactobacillaceaespecies as the most abundant species in the gut. For metaproteomics analysis, peptides were generated by using the Fasp method and subsequently fractionated by strong anion exchanges. Metaproteomics analysis identified 3,673 proteins. Among the most frequently identified proteins, 380 proteins belonged toLactobacillusspp., 155 belonged toClostridiumspp., and 66 belonged toStreptococcusspp. The most frequently identified proteins were heat shock chaperones, including 349 GroEL proteins, from many bacterial species, whereas the most abundant enzymes were pyruvate kinases, as judged by the number of peptides identified per protein (spectral counting). Gene ontology and KEGG pathway analyses revealed the functions and locations of the identified proteins. The findings of both metaproteomics and 16S rRNA sequencing analyses are discussed.

scholarly journals Global genomic similarity and core genome sequence diversity of the Streptococcus genus as a toolkit to identify closely related bacterial species in complex environments

PeerJ ◽  
2019 ◽  
Vol 6 ◽  
pp. e6233 ◽  
Author(s):  
Hugo R. Barajas ◽  
Miguel F. Romero ◽  
Shamayim Martínez-Sánchez ◽  
Luis D. Alcaraz
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Core Genome ◽  
Bacterial Species ◽  
Genomic Diversity ◽  
Comparative Genomic ◽  
Rrna Gene ◽  
Gene Phylogeny ◽  
The Core ◽  
Core Proteins

Background The Streptococcus genus is relevant to both public health and food safety because of its ability to cause pathogenic infections. It is well-represented (>100 genomes) in publicly available databases. Streptococci are ubiquitous, with multiple sources of isolation, from human pathogens to dairy products. The Streptococcus genus has traditionally been classified by morphology, serum types, the 16S ribosomal RNA (rRNA) gene, and multi-locus sequence types subject to in-depth comparative genomic analysis. Methods Core and pan-genomes described the genomic diversity of 108 strains belonging to 16 Streptococcus species. The core genome nucleotide diversity was calculated and compared to phylogenomic distances within the genus Streptococcus. The core genome was also used as a resource to recruit metagenomic fragment reads from streptococci dominated environments. A conventional 16S rRNA gene phylogeny reconstruction was used as a reference to compare the resulting dendrograms of average nucleotide identity (ANI) and genome similarity score (GSS) dendrograms. Results The core genome, in this work, consists of 404 proteins that are shared by all 108 Streptococcus. The average identity of the pairwise compared core proteins decreases proportionally to GSS lower scores, across species. The GSS dendrogram recovers most of the clades in the 16S rRNA gene phylogeny while distinguishing between 16S polytomies (unresolved nodes). The GSS is a distance metric that can reflect evolutionary history comparing orthologous proteins. Additionally, GSS resulted in the most useful metric for genus and species comparisons, where ANI metrics failed due to false positives when comparing different species. Discussion Understanding of genomic variability and species relatedness is the goal of tools like GSS, which makes use of the maximum pairwise shared orthologous sequences for its calculation. It allows for long evolutionary distances (above species) to be included because of the use of amino acid alignment scores, rather than nucleotides, and normalizing by positive matches. Newly sequenced species and strains could be easily placed into GSS dendrograms to infer overall genomic relatedness. The GSS is not restricted to ubiquitous conservancy of gene features; thus, it reflects the mosaic-structure and dynamism of gene acquisition and loss in bacterial genomes.

scholarly journals The Oral Microbiome of Healthy Japanese People at the Age of 90

2020 ◽  
Vol 10 (18) ◽  
pp. 6450 ◽  
Author(s):  
Yoshiaki Nomura ◽  
Erika Kakuta ◽  
Noboru Kaneko ◽  
Kaname Nohno ◽  
Akihiro Yoshihara ◽  
...  
Keyword(s):  
Human Microbiome ◽  
Oral Microbiome ◽  
Rrna Gene ◽  
Core Microbiome ◽  
Operational Taxonomic Units ◽  
The Core ◽  
Hypervariable Regions ◽  
Study Population ◽  
Granulicatella Adiacens

For a healthy oral cavity, maintaining a healthy microbiome is essential. However, data on healthy microbiomes are not sufficient. To determine the nature of the core microbiome, the oral-microbiome structure was analyzed using pyrosequencing data. Saliva samples were obtained from healthy 90-year-old participants who attended the 20-year follow-up Niigata cohort study. A total of 85 people participated in the health checkups. The study population consisted of 40 male and 45 female participants. Stimulated saliva samples were obtained by chewing paraffin wax for 5 min. The V3–V4 hypervariable regions of the 16S ribosomal RNA (rRNA) gene were amplified by PCR. Pyrosequencing was performed using MiSeq. Operational taxonomic units (OTUs) were assigned on the basis of a 97% identity search in the EzTaxon-e database. Using the threshold of 100% detection on the species level, 13 species were detected: Streptococcus sinensis, Streptococcus pneumoniae, Streptococcus salivarius, KV831974_s, Streptococcus parasanguinis, Veillonella dispar, Granulicatella adiacens, Streptococcus_uc, Streptococcus peroris, KE952139_s, Veillonella parvula, Atopobium parvulum, and AFQU_vs. These species represent potential candidates for the core make-up of the human microbiome.

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