- Research article
- Open Access
Defining the healthy "core microbiome" of oral microbial communities
© Zaura et al; licensee BioMed Central Ltd. 2009
Received: 10 July 2009
Accepted: 15 December 2009
Published: 15 December 2009
Most studies examining the commensal human oral microbiome are focused on disease or are limited in methodology. In order to diagnose and treat diseases at an early and reversible stage an in-depth definition of health is indispensible. The aim of this study therefore was to define the healthy oral microbiome using recent advances in sequencing technology (454 pyrosequencing).
We sampled and sequenced microbiomes from several intraoral niches (dental surfaces, cheek, hard palate, tongue and saliva) in three healthy individuals. Within an individual oral cavity, we found over 3600 unique sequences, over 500 different OTUs or "species-level" phylotypes (sequences that clustered at 3% genetic difference) and 88 - 104 higher taxa (genus or more inclusive taxon). The predominant taxa belonged to Firmicutes (genus Streptococcus, family Veillonellaceae, genus Granulicatella), Proteobacteria (genus Neisseria, Haemophilus), Actinobacteria (genus Corynebacterium, Rothia, Actinomyces), Bacteroidetes (genus Prevotella, Capnocytophaga, Porphyromonas) and Fusobacteria (genus Fusobacterium).
Each individual sample harboured on average 266 "species-level" phylotypes (SD 67; range 123 - 326) with cheek samples being the least diverse and the dental samples from approximal surfaces showing the highest diversity. Principal component analysis discriminated the profiles of the samples originating from shedding surfaces (mucosa of tongue, cheek and palate) from the samples that were obtained from solid surfaces (teeth).
There was a large overlap in the higher taxa, "species-level" phylotypes and unique sequences among the three microbiomes: 84% of the higher taxa, 75% of the OTUs and 65% of the unique sequences were present in at least two of the three microbiomes. The three individuals shared 1660 of 6315 unique sequences. These 1660 sequences (the "core microbiome") contributed 66% of the reads. The overlapping OTUs contributed to 94% of the reads, while nearly all reads (99.8%) belonged to the shared higher taxa.
We obtained the first insight into the diversity and uniqueness of individual oral microbiomes at a resolution of next-generation sequencing. We showed that a major proportion of bacterial sequences of unrelated healthy individuals is identical, supporting the concept of a core microbiome at health.
The commensal human microbiome is estimated to outnumber the amount of human body cells by a factor of ten . These complex microbial communities are normal residents of the skin, the oral cavity, vaginal and intestinal mucosa and carry a broad range of functions indispensable for the wellbeing of the host . Usually we only become aware of their presence when the balance between the microbiota and the host is lost, and disease is manifest. This is reflected in the ample knowledge on the human microbiome at the state of disease as opposed to the limited picture we have of the healthy microbiome. In order to diagnose and treat disease at an early and reversible stage one needs to describe the commensal microbiome associated with health. For example, understanding changes in the oral microbiome at the early stages of periodontitis and dental caries, the most prevalent chronic oral diseases, would allow diagnosis and treatment before the appearance of periodontal pockets or dental hard tissue loss.
Recent advances in sequencing technology, such as 454 pyrosequencing provides hundreds of thousands of nucleotide sequences at a fraction of the cost of traditional methods . This deep sequencing has revealed an unexpectedly high diversity of the human oral microbiome: dental plaque pooled from 98 healthy adults comprised about 10000 microbial phylotypes . This is an order of magnitude higher than previously reported 700 oral microbial phylotypes as identified by cultivation or traditional cloning and sequencing . Moreover, by pooling about 100 individual microbiomes and pyrosequencing these, the ecosystem still appeared undersampled: the ultimate diversity of the oral microbiome was estimated to be around 25000 phylotypes .
If "everything is everywhere, but, the environment selects" , then a healthy oral microbiome should be dominated by a "core microbiome" characteristic for health. These abundant phylotypes would maintain the functional stability and homeostasis necessary for a healthy ecosystem. To date though, there is no information available on how many of the 25000 phylotypes  actually contribute to a single oral cavity and how common or exclusive individual oral microbiomes of unrelated healthy individuals are.
The oral cavity differs from all other human microbial habitats by the simultaneous presence of two types of surfaces for microbial colonization: shedding (mucosa) and solid surfaces (teeth or dentures). This intrinsic property of the oral cavity provides immense possibilities for a diverse range of microbiota. Once the symbiotic balance between the host and the microbiota is lost, these microbiota may become involved in disease. For instance, the tongue, with its mucosal 'crypts' which allow anaerobic microbiota to flourish, is an established source of halitosis . Approximal (adjoining) surfaces between adjacent teeth have limited access to fluorides and saliva, and therefore have a predilection for dental caries . To gather as complete information as possible on the healthy oral microbiome, microbial samples should be obtained from various ecological niches throughout the oral cavity.
Here we present the first description of diversity, uniqueness and the level of overlap of microbiomes of three healthy individual oral cavities at various intraoral niches (different dental surfaces, cheek, hard palate, tongue and saliva) at the probing depth as provided by targeted pyrosequencing of the V5-V6 hypervariable region of the small subunit ribosomal RNA.
Results and Discussion
The overall sequence data
In total, 452071 reads passed the quality control filters. Recent publications [9, 10] have identified the potential inflation of richness and diversity estimates caused by low-quality reads (pyrosequencing noise). Reads with multiple errors can form new OTUs if they are more distant from their real source than the clustering width. These reads are relatively rare and most commonly occur as singletons or doubletons. To preclude the inclusion of sequencing artifacts or potential contaminants from sample processing, and to avoid diversity overestimation, we included only sequences occurring at least five times in further analyses. By doing so, we have also removed many less frequent but valid sequences representing the rare members of the microbiome.
Participant details and number of sequences, OTUs and higher taxa.
OTUs at 3% Differenceb
OTUs at 6% Differenceb
OTUs at 10% Differenceb
Distribution of reads, unique sequences, OTUs and shared microbiome (sequences and OTUs) per phylum.
Number of Reads (% of all)a
Unique Sequences (% of all)a
Number of Shared Sequencesb
% of Reads with Shared Sequences
Number of OTUs (% of all)c
Number of Shared OTUsd
% of Reads with Shared OTUs
Clustering of the overall data in phylotypes
Clustering the unique sequences into operational taxonomic units (OTUs) at a 3% genetic distance resulted in 818 different OTUs (Table 1, Additional file 1). A 97% identity in 16S rRNA gene sequences is commonly used to group "species-level" phylotypes [1, 11, 12]. A 3% variation within a short hypervariable region of the small subunit (SSU) rRNA gene may not correlate exactly with a 3% variation along the entire SSU rRNA gene. In fact, the correlation between genetic differences may well vary with different regions of the gene, and in different classes of organisms. However, most microbial diversity projects to date have used 3% OTUs [1, 13, 14], and to be consistent with other research using pyrosequencing sequences we have chosen to use 3% OTUs as well. We have also clustered sequences into OTUs using more conservative genetic differences of 6% and 10% (Table 1, Additional file 2, Additional file 3). In the further text however we refer only to OTUs at the 3% difference. These OTUs were grouped in 112 higher taxa (Additional file 4) consisting of 78 genera and 34 more inclusive taxa (e.g., family, order, class), representing eight bacterial phyla (Table 2).
Diversity and taxonomy of individual microbiomes
About 100 "species-level" phylotypes (118, 97 and 112 phylotypes in the microbiome of individual S1, S2 and S3, respectively) belonged to abundant OTUs of the individual microbiome (Additional file 1). A phylotype was considered abundant if it contributed to at least 0.1% of the microbiome. These abundant phylotypes together contributed to 92 - 93% of each microbiome.
As with a pooled oral microbiome  and individually sequenced gut microbiomes , each individual oral microbiome in this study was dominated by a few sequences while most sequences were rare and contributed to the "long tail" effect (Figure 2).
Overlap of three individual oral microbiomes
Relative abundance of individual-specific ("exclusive") sequences
% Sequences "Exclusive"
% of Reads with "Exclusive" Sequences
Taxonomy of Predominant "Exclusive" Sequencesa
% of Reads
Nr of Samplesb
Sixty-nine, 43 and 91 OTUs originated from one particular microbiome and contributed to 3.9%, 0.5% and 0.9% of the microbiome from individual S1, S2 and S3, respectively. Interestingly, all unique OTUs from either S2 or S3 were present at low abundance, while in S1 four of 69 unique phylotypes were relatively abundant (≥ 0.1% of the microbiome). One phylotype (OTU ID 774, Pasteurellaceae) contributed to 2.2% of this microbiome and was preferentially found around the molar tooth (buccal, lingual and approximal surfaces of tooth 16) and in the sample obtained at the hard palate.
The OTUs representing different phyla were not equally shared among the individuals (Table 2). The lowest similarity was observed in Spirochaetes (25% common OTUs), followed by Bacteroidetes and Cyanobacteria (33%), Proteobacteria (42%), Actinobacteria (48%), candidate division TM7 (50%), Firmicutes (57%), while the highest similarity was found in Fusobacteria (62%). The low similarity among the OTUs of Spirochaetes among the three microbiomes could be due to low abundance of this phylum in the different samples. Since a high prevalence of Spirochaetes in dental plaque is associated with periodontal disease , it would be interesting to assess the degree of similarity and diversity of these phylotypes in a group of periodontitis patients.
At the higher taxonomic levels, 72% of all taxa (genus level or above) were shared by the three microbiomes, contributing to 99.8% of all reads. Only 2-11% of higher taxa were individual-specific (Figure 3C, Additional file 4). However, these taxa were found at a very low abundance (5-49 reads) and most likely were not a part of the commensal oral flora, and should be regarded as "transients".
The observed overlap in taxa and in phylotypes is unexpectedly high and considerably higher than the recently reported average of 13% similarity in phylotypes between any two hands from unrelated individuals . Of even greater contrast to our findings are the comparisons of gut microbiomes which show no overlap in microbiota in unrelated individuals . Instead of a core microbiome at an organismal lineage level, gut microbiomes harboured distinct core genes . The most probable explanation in the observed exclusiveness of gut microbiomes is the close interplay of intestinal microbiota with the host.
In the abovementioned study on hand surface microbiomes, only five phylotypes were shared across the 102 hands sampled . Human palms are continuously exposed to diverse biological and abiotic surfaces that may function as a microbial source, and furthermore, hands are regularly washed, allowing new communities of different origins to establish. This may explain the high diversity and relatively low overlap in hand palm communities. The situation is cardinally different in the oral cavity. Even though dental hygiene procedures (toothbrushing, flossing) effectively removes dental plaque, newly cleaned surfaces are continuously bathed in saliva. Saliva functions here as a transport medium for microorganisms from sites that were not affected by cleansing (tongue and other mucosal sites, gingival crevices, anatomical irregularities on tooth surfaces etc). Furthermore, the human mouth is a relatively stable ecosystem regarding temperature and saliva as a nutrient source. The contact of the oral cavity with external microbial sources is highest in the first years of human life , and is mostly limited to microorganisms in food or drinking water at a later age.
Sample-specific profiles within individual oral microbiomes
Fifteen taxa were found at all sites in all three individuals: thegenera Streptococcus, Neisseria, Corynebacterium, Rothia, Actinomyces, Haemophilus, Prevotella, Fusobacterium, Granulicatella, Capnocytophaga, representatives of the Veillonellaceae, Neisseriaceae and Pasteurellaceae families, the Bacteroidales order and unclassified Firmicutes. Unclassified Bacteria and an additional four taxa were found in all but one sample: genus Porphyromonas, Leptotrichia, TM7 genera incertae sedis and Campylobacter (Additional file 6).
As mentioned above (Figure 2), a few sequences dominated each individual microbiome. Three of the sequences were found across all 29 samples that originated from three individuals: two Veillonellaceae family members (phylum Firmicutes) and one Fusobacterium genus member (phylum Fusobacteria). This latter ubiquitous sequence accounted for 34% of Fusobacterium reads and for 1% of the total reads (Additional file 5). The latter finding is especially interesting in the light of the central role fusobacteria play in mediating coaggregation of non-aggregating microbiota and their importance as a structural component of both healthy and disease-associated dental plaque .
Within an individual oral cavity, 36 - 51% of the unique sequences were found solely in a single sample and mostly at a low abundance. About 600-750 sequences per individual were found only once. Among these, numerous representatives of commensal oral microorganisms, as well as non-commensal microbiota, such as Vibrio, Salinivibrio and other Gammaproteobacteria were present. Even though these sequences were found as singletons in a particular microbiome, they had to be present at least five times across all three microbiomes according to the cut-off we applied.
Not all sequences that were found at a single site were rare: 16 of the sample-specific sequences (ten, two and four sequences in individuals S1, S2 and S3, respectively) were found at least 100 times (maximum 321 times) in a particular sample (data not shown). Surprisingly, all four abundant sample-specific sequences from volunteer S3 (two streptococci, Granulicatella and Corynebacterium) and five of the ten abundant sample-specific sequences from volunteer S1 (three streptococci, Haemophilus and Acidovorax) were found solely in the saliva sample of the respective individuals. The relatively high abundance of these saliva-specific organisms suggests that they are a part of the commensal oral microbiota. The most likely source of these organisms is a niche that was not specifically sampled but was exposed to saliva, e.g., tonsils, back of the tongue or subgingival plaque. Tonsils, for instance, have been shown to harbour a more diverse community than intraoral mucosal or dental sites .
A trend for a higher diversity was observed in the samples taken at the approximal surfaces and the lingual surface of the front teeth (Figure 6B). The approximal surfaces, also known as plaque stagnations sites, are protected from regular toothbrushing. Although volunteers were asked to brush their teeth 12 hr before the samples were collected, the use of interdental oral hygiene means such as floss or toothpicks was not controlled. It is likely that older and thus more diverse plaque  was sampled at these sites. Higher diversity of the plaque from the lingual surface of the front tooth but not that of the molar tooth suggests that the composition of plaque of the lingual surface of the front tooth might be influenced by the anatomy of this surface - a protruding rounded tubercle at the gingival third of the crown, near the gingival sulcus. The area near the sulcus, protected by the tubercle, may have provided a niche suitable for more diverse microorganisms than anatomically flat lingual surface of the molar.
The two cheek samples from individual S1 and individual S3 showed the lowest diversity among all samples (Figure 6B). These samples were dominated by only two OTUs each, identified as streptococci, with 70 sequences comprising 13% of all reads in the sample from S1, and 46 sequences comprising 17% of the reads in the cheek sample from S3. The closest match to these OTUs was Streptococcus mitis which is known to produce immunoglobulin A1 protease. This enzyme is important for the ability of bacteria to colonize mucosal membranes in the presence of S-IgA antibodies in saliva  and might explain high dominance of these phylotypes in these particular samples. Notably, the cheek sample from S3 still contained one of the highest counts of taxa (234 phylotypes), but obviously at a very low abundance.
In order to explore if the location in the oral cavity has an effect on the microbiota of the particular niche (lingual, buccal or approximal surface of the tooth), we sampled two distant teeth - the front tooth and the first molar. No pattern could be found among the samples from individual S2. However, both distantly situated lingual samples from individual S1 and S3, as well as both approximal samples from individual S3, showed higher similarity than the buccal samples of the respective individual (Figure 7A-B). The differences in the intraoral conditions such as salivary flow, lip or cheek movement, chewing forces and food clearance, may have had a higher impact on buccal than lingual or approximal surfaces of the two regions of the oral cavity.
The major proportion of oral microbiomes was common across three unrelated healthy individuals, supporting the concept of a core-microbiome at health. The site specificity of the oral microbiome, especially between mucosal and dental sites and between saliva and dental sites, should be considered in future study designs. Sequencing large sub-populations in longitudinal clinical trials at defined intermediate stages from health to disease will provide oral health professionals with valuable information for future diagnostic and treatment modalities.
Three healthy Caucasian male adults (Table 1) with no antibiotic use in the past three months participated in the study after signed informed consent. The study was approved by the Medical Ethical Committee of the Free University Amsterdam. Each individual had a full set of natural dentition and none of them wore any removable or fixed prosthetic appliances, they had no clinical signs of oral mucosal disease and did not suffer from halitosis, did not have caries (white spot lesions of enamel or dentin lesions) or periodontal disease. The periodontal health was defined as no periodontal pockets deeper than 3 mm and no bleeding on probing at more than 10% of gingival sites. The sites that were sampled did not show any bleeding. In selecting healthy volunteers for experimental gingivitis studies, gingiva is considered healthy if bleeding on marginal probing is present at less than 20-25% of gingival sites [24, 25].
Samples were collected in the morning, 12 hr after tooth brushing and 2 hr after the last food and/or drink intake. Parafilm-chewing stimulated saliva was collected and mixed 1:2 with RNAProtect (Qiagen, Hilden, Germany). For supragingival plaque sampling, three intact dental surfaces around a single upper incisor (tooth 11 buccally, lingually, and approximal surfaces of teeth 11/12) and around an upper molar (tooth 16 buccally, lingually, and approximal surfaces of teeth 15/16) were selected. Mucosal swabs were collected from the cheek, hard palate and tongue surface. The mucosal and dental surface swabs were collected using a sterile microbrush (Microbrush International, Grafton, USA). To sample buccal and lingual dental surfaces, the microbrush was moved over the enamel from mesial to distal curvature of the tooth crown along the gingival margin and tooth-surface border. The cheek mucosa and hard palate were sampled by making a circular motion of the microbrush over the central part of cheek mucosa or hard palate while applying slight pressure. The tongue swab was collected by several strokes over the first two thirds of the tongue dorsum in anterior-posterior direction. After the sample was taken, the tip of the microbrush was placed into an Eppendorf vial with 0.2 ml RNAProtect solution and clipped off. Interproximal plaque from the approximal surfaces (11/12 and 15/16) was collected with unwaxed dental floss (Johnson & Johnson, Almere, the Netherlands). A piece of floss was carefully slid over the contact point and moved slowly upwards along both neighbouring approximal surfaces. Then one end of the floss was released and the floss was slowly pulled through the interdental space avoiding the contact with gingiva. Plaque was removed from the dental floss by drawing it through a slit cut in the lid of a Eppendorf vial  containing 0.2 ml RNAProtect solution. One sample (buccal molar surface) from individual S2 was lost in sample processing. All samples were stored at -80°C until further processing for DNA extraction.
A 0.35-ml quantity of lysis buffer (AGOWA mag Mini DNA Isolation Kit, AGOWA, Berlin, Germany) was added to plaque and mucosal swab samples. A 0.1-ml quantity of saliva sample was transferred to a sterile screw-cap Eppendorf tube with 0.25 ml of lysis buffer. Then 0.3 g zirconium beads (diameter, 0.1 mm; Biospec Products, Bartlesville, OK, USA) and 0.2 ml phenol were added to each sample. The samples were homogenized with a Mini-beadbeater (Biospec Products) for 2 min. DNA was extracted with the AGOWA mag Mini DNA Isolation Kit (AGOWA, Berlin, Germany) and quantified (Nanodrop ND-1000; NanoDrop Technologies, Montchanin, DE, USA).
PCR amplicon libraries of the small subunit ribosomal RNA gene V5-V6 hypervariable region were generated for the individual samples. PCR was performed using the forward primer 785F (GGATTAGATACCCBRGTAGTC) and the reverse primer 1061R (TCACGRCACGAGCTGACGAC). The primers included the 454 Life Sciences (Branford, CT, USA) Adapter A (for forward primers) and B (for reverse primers) fused to the 5' end of the 16S rRNA bacterial primer sequence and a unique trinucleotide sample identification key.
The amplification mix contained 2 units of Goldstar DNA polymerase (Eurogentec, Liège, Belgium), 1 unit of Goldstar polymerase buffer (Eurogentec), 2.5 mM MgCl2, 200 μM dNTP PurePeak DNA polymerase Mix (Pierce Nucleic Acid Technologies, Milwaukee, WI), 1.5 mM MgSO4 and 0.2 μM of each primer. After denaturation (94°C; 2 min), 30 cycles were performed that consisted of denaturation (94°C; 30 sec), annealing (50°C; 40 sec), and extension (72°C; 80 sec). DNA was isolated by means of the MinElute kit (Qiagen, Hilden, Germany). The quality and the size of the amplicons were analyzed on the Agilent 2100 Bioanalyser with the DNA 1000 Chip kit (Agilent Technologies, Santa Clara, CA, USA) and quantified using Nanodrop ND-1000 spectrophotometer. The amplicon libraries were pooled in equimolar amounts in two separate pools. Each pool was sequenced unidirectionally in the reverse direction (B-adaptor) by means of the Genome Sequencer FLX (GS-FLX) system (Roche, Basel, Switzerland). Sequences are available at the Short Read Archive of the National Center for Biotechnology Information (NCBI) [NCBI SRA: SRP000913].
GS-FLX sequencing data were processed as previously described . In brief, we trimmed sequences by removing primer sequences and low-quality data, sequences that did not have an exact match to the reverse primer, that had an ambiguous base call (N) in the sequence, or that were shorter than 50 nt after trimming. We then used the GAST algorithm  to calculate the percent difference between each unique sequence and its closest match in a database of 69816 unique eubacterial and 2779 unique archaeal V5-V6 sequences, representing 323499 SSU rRNA sequences from the SILVA database . Taxa were assigned to each full-length reference sequence using several sources including Entrez Genome entries, cultured strain identities, SILVA, and the Ribosomal Database Project Classifier . In cases where reads were equidistant to multiple V5-V6 reference sequences, and/or where identical V5-V6 sequences were derived from longer sequences mapping to different taxa, reads were assigned to the lowest common taxon of at least two-thirds of the sequences. The operational taxonomic units (OTUs) were created by aligning unique sequences and calculating distance matrices as previously described  and using DOTUR  to create clusters at the 0.03, 0.06 and 0.1 level.
Only sequences that were found at least 5 times were included in the analyses. This strict and conservative approach was chosen to preclude inclusion of sequences from potential contamination or sequencing artefacts. To compare the relative abundance of OTUs among samples, the data were normalized for number of sequenced reads obtained for each sample. To reduce the influence of abundant taxa on principal component analyses, the normalized abundance data were log2 transformed. Shannon Diversity Index (H' = -Σ p i ln(p i ) where p i is the proportion of taxon i) and Principal component analysis (PCA) were performed in PAST v. 1.89 . The Venn diagrams were made with Venn Diagram Plotter v. 1.3.3250.34910 (Pacific Northwest National Laboratory http://www.pnl.gov/; http://omics.pnl.gov/. Spearman correlation between the size of OTUs and the number of unique sequences within each OTU was calculated using SPSS (Version14.0).
We thank Mieke Havekes, Louise Nederhoff, Mark Buijs and Michel Hoogenkamp for technical assistance; Maximiliano Cenci, Tatiana Pereira and Duygu Kara for clinical assistance. Sue Huse was supported on a subcontract to Mitchell L. Sogin from the Woods Hole Center for Oceans and Human Health, funded by the National Institutes of Health and National Science Foundation (NIH/NIEHS1 P50 ES012742-01 and NSF/OCE 0430724). We also thank the ACTA Research Institute and GABA International for financial support.
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