Egija ZaurapersName, Bart J. F. KeijserpersName, Susan M. HusepersName, Wim CrielaardpersName (2009date). “Defining the healthy “core microbiome” of oral microbial communities.” BMC Microbiologytitle·venue.
Object of study — A CLASS WHOSE MEMBERSHIP IS DECIDED BY HOW MANY PEOPLE YOU LOOKED AT - the 'core microbiometerm·def' of the healthy mouth, defined as the sequences shared by THREE unrelated volunteers across 29 intraoral sites
Research question — how many of the oral microbiome's estimated 25,000 phylotypesterm are present in one mouth, and how much of that is common to unrelated healthy people
Contribution — the first pyrosequencing description of whole individual oral microbiomes at site resolution, and the finding that 65% of unique sequences, 75% of OTUs and 84% of higher taxa are shared
phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text| phenomenon | a health-defined class assembled from an intersection, in a field whose own estimates say a hundred people still undersample the ecosystem — “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 [ 4 ]. This is an order of magnitude higher than previously reported 700 oral microbial phylotypes as identified by cultivation or traditional cloning and sequencing [ 5 ]. 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 [ 4 ]. If "everything is everywhere, but, the environment selects" [ 6 ], 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 [ 4 ] actually contribute to a single oral cavity and how common or exclusive individual oral microbiomes of unrelated healthy individuals are.” | |
| claim-type | existence — “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.” | |
| method — evidence | — “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.” | |
| object of study — evidence | — “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.” | |
| research question — evidence | — “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 [ 4 ]. This is an order of magnitude higher than previously reported 700 oral microbial phylotypes as identified by cultivation or traditional cloning and sequencing [ 5 ]. 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 [ 4 ]. If "everything is everywhere, but, the environment selects" [ 6 ], 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 [ 4 ] actually contribute to a single oral cavity and how common or exclusive individual oral microbiomes of unrelated healthy individuals are.” | |
| contribution — evidence | — “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.” | |
| mechanism | THE `CORE` IS THE INTERSECTION OF THREE PEOPLE: `The three individuals shared 1660 of 6315 unique sequences. These 1660 sequences (the `CORE MICROBIOME`) contributed 66% of the reads` - and the participants are `Three healthy Caucasian male adults`. Add a fourth person and the core can only shrink. The corpus holds classes cut from continua, classes bounded by apparatus and classes that ARE their threshold; this is the first whose extension is a function of the SAMPLE SIZE of the study that named it — “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.” | mechanisms[0] |
| mechanism | AND ONE DATASET GIVES 66%, 93% OR 99.8% SHARED DEPENDING ON THE UNIT COUNTED: `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 overlapping OTUs contributed to 94% of the reads, while nearly all reads (99.8%) belonged to the shared higher taxa.` Same mouths, same reads, same question - and the answer moves by 34 percentage points on the choice of what to count as a thing — “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.” | mechanisms[1] |
| mechanism | AND THE SPECIES THRESHOLD IS KEPT FOR COMPARABILITY WHILE ITS MEANING IS DENIED: `A 3% VARIATION WITHIN A SHORT HYPERVARIABLE REGION of the SSU rRNA gene may not correlate exactly with A 3% variation along the entire SSU rRNA GENE. In fact, the correlation ... 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, and to be consistent with other research ... we have chosen to use 3% OTUs as well.` The fourth `keep the suboptimal thing for continuity` in this corpus - and the first where what is retained is a NUMBER rather than a word — “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.” | mechanisms[2] |
| mechanism | AND THE FILTER ADMITS IT DELETES TRUE POSITIVES: `To preclude the inclusion of sequencing artifacts or potential contaminants ... 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.` A published statement of what a cleaning rule costs, in the same paragraph as the rule — “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.” | mechanisms[3] |
| mechanism | AND THE FILTER'S UNIT IS NOT THE CLAIM'S UNIT, WHICH LEAKS: `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.` A sequence qualifies for one person's microbiome because it was abundant in the other two - so the per-individual counts are not independent of each other, and the shared fraction is measured on data the sharing helped create — “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.” | mechanisms[4] |
| mechanism | AND THE COST TO RESOLUTION IS STATED AS A NUMBER: `The stringent selection of sequences (the cut-off of 5 reads) and individual labelling and sequencing of 29 samples on a single pyrosequencing plate have largely reduced the depth of pyrosequencing resolution. On average, 10000 reads per sample were obtained instead of the 400000 reads possible when using a full plate for a single sample. Our findings on diversity, therefore, should be considered conservative.` A fortyfold reduction, declared — “The stringent selection of sequences (the cut-off of 5 reads) and individual labelling and sequencing of 29 samples on a single pyrosequencing plate have largely reduced the depth of pyrosequencing resolution. On average, 10000 reads per sample were obtained instead of the 400000 reads possible when using a full plate for a single sample. Our findings on diversity, therefore, should be considered conservative.” | mechanisms[5] |
| mechanism | AND `HEALTHY` IS OPERATIONALISED STRICTER THAN THE FIELD'S OWN CONVENTION, WHICH IS ITSELF A RANGE: `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 ... 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.` The qualifier in the title rests on a bar set at half the convention, and the convention is quoted as `20-25%` rather than a number — “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 ].” | mechanisms[6] |
| mechanism | AND THE JUSTIFICATION FOR EXPECTING A CORE AT ALL IS A QUOTED APHORISM: `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.` The class is predicted by a century-old maxim before it is measured — “If "everything is everywhere, but, the environment selects" [ 6 ], 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.” | mechanisms[7] |
| mechanism | AND THE FIELD'S OWN ESTIMATE SAYS A HUNDRED PEOPLE UNDERSAMPLE IT: `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 ... by pooling about 100 individual microbiomes ... the ecosystem still appeared undersampled: the ultimate diversity ... was estimated to be around 25000 phylotypes.` 700, then 10,000, then 25,000 - and this study has three mouths — “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 [ 4 ]. This is an order of magnitude higher than previously reported 700 oral microbial phylotypes as identified by cultivation or traditional cloning and sequencing [ 5 ]. 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 [ 4 ]. If "everything is everywhere, but, the environment selects" [ 6 ], 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 [ 4 ] actually contribute to a single oral cavity and how common or exclusive individual oral microbiomes of unrelated healthy individuals are.” | mechanisms[8] |
| mechanism | AND WHAT IS NOT SHARED IS RENAMED RATHER THAN COUNTED: taxa `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`.` A residual category created by the abundance filter and then given a name that explains it away - and elsewhere the paper argues the opposite for saliva-specific sequences, whose `relatively high abundance ... suggests that they are a part of the commensal oral microbiota`. Abundance decides membership in both directions — “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".” | mechanisms[9] |
| mechanism | AND THE HABITAT'S DEFINING PROPERTY IS A PAIR OF SURFACE TYPES: `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).` The site effect that follows - saliva profiles closer to mucosa than to teeth - is the reason a single `oral microbiome` is a coarser object than it sounds — “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.” | mechanisms[10] |
| mechanism | AND ONE SAMPLE CAN BE BOTH THE LEAST DIVERSE AND AMONG THE RICHEST: `The two cheek samples ... showed THE LOWEST DIVERSITY among all samples. These samples were dominated by only two OTUs EACH ... Notably, the cheek sample from S3 still contained one of the highest counts of taxa (234 phylotypes), but obviously at a very low abundance.` Diversity by Shannon index and diversity by count disagree on the same sample - the unit problem again, one figure apart — “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 [ 22 ] 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.” | mechanisms[11] |
| extends | the pooled-plaque pyrosequencing survey of 98 healthy adults that reported ~10,000 phylotypes and estimated ~25,000 in total - this paper asks how many reach a single mouth — “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 [ 4 ]. This is an order of magnitude higher than previously reported 700 oral microbial phylotypes as identified by cultivation or traditional cloning and sequencing [ 5 ]. 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 [ 4 ]. If "everything is everywhere, but, the environment selects" [ 6 ], 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 [ 4 ] actually contribute to a single oral cavity and how common or exclusive individual oral microbiomes of unrelated healthy individuals are.” | claim_relations[0] |
| critiques | diversity estimates from cultivation and cloning (4-28 species per site) and 16S microarrays (10-81), an order of magnitude below what pyrosequencing finds — “On average, each individual sample harboured 266 "species-level" phylotypes (SD 67; range 123 - 326) (Figure 6A ). This is again considerably higher than the previously reported 4 - 28 species per site using traditional cloning and sequencing methods [ 15 ] or 10 - 81 species using a 16S rRNA gene-based microarray [ 20 ].” | claim_relations[1] |
| extends | the Baas Becking maxim 'everything is everywhere, but, the environment selects', used to predict that a core exists before it is measured — “If "everything is everywhere, but, the environment selects" [ 6 ], 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.” | claim_relations[2] |
| sense | `core microbiome` (this paper) - THE 1660 UNIQUE SEQUENCES PRESENT IN ALL THREE SAMPLED INDIVIDUALS, contributing 66% of reads; an intersection, so its size is a function of how many people were sequenced — “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.” | senses[0] |
| sense | `species-level phylotype` - `OTUs clustering sequences at a 3% genetic difference` in the V5-V6 region only, a convention the authors adopt for comparability while saying it `may not correlate exactly` with 3% over the whole gene — “Phylotypes were defined as OTUs clustering sequences at a 3% genetic difference. The highest taxon (in most cases, genus)” | senses[1] |
| sense | `healthy` (as operationalised here) - no pockets deeper than 3 mm and bleeding at no more than 10% of gingival sites, against a literature convention of `less than 20-25%` — “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 ].” | senses[2] |
| sense | `transients` - the low-abundance taxa (5-49 reads) the paper judges `not a part of the commensal oral flora`; a residue of the abundance filter, given a name — “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".” | senses[3] |
| control | discarding every sequence seen fewer than five times, to exclude sequencing artefacts and contaminants — partial — “The stringent selection of sequences (the cut-off of 5 reads) and individual labelling and sequencing of 29 samples on a single pyrosequencing plate have largely reduced the depth of pyrosequencing resolution. On average, 10000 reads per sample were obtained instead of the 400000 reads possible when using a full plate for a single sample. Our findings on diversity, therefore, should be considered conservative.” — rules out pyrosequencing noise inflating richness - and also, as stated, many valid rare members — “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.” | controls[0] |
| control | applying the five-read threshold ACROSS all three microbiomes rather than within each — failed — “On the other hand, 17-19% of the unique sequences originating from a single oral cavity were not shared with either of the other two microbiomes (Table 3 ).” — rules out nothing cleanly - a sequence seen once in one mouth is retained there because it was abundant in the other two, so the individual profiles are not independent — “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.” | controls[1] |
| control | an explicit clinical health screen - no caries, no periodontal disease, no halitosis, no antibiotics in three months, bleeding below 10% of sites — held — “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 ].” — rules out disease-associated microbiota entering a description of the healthy state — “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.” | controls[2] |
| weight | the pooled-plaque pyrosequencing survey of 98 healthy adults that reported ~10,000 phylotypes and estimated ~25,000 in total - this paper asks how many reach a single mouth — 0.9 — “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 [ 4 ]. This is an order of magnitude higher than previously reported 700 oral microbial phylotypes as identified by cultivation or traditional cloning and sequencing [ 5 ]. 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 [ 4 ]. If "everything is everywhere, but, the environment selects" [ 6 ], 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 [ 4 ] actually contribute to a single oral cavity and how common or exclusive individual oral microbiomes of unrelated healthy individuals are.” | claim_relations[0] |
| weight | diversity estimates from cultivation and cloning (4-28 species per site) and 16S microarrays (10-81), an order of magnitude below what pyrosequencing finds — 0.85 — “On average, each individual sample harboured 266 "species-level" phylotypes (SD 67; range 123 - 326) (Figure 6A ). This is again considerably higher than the previously reported 4 - 28 species per site using traditional cloning and sequencing methods [ 15 ] or 10 - 81 species using a 16S rRNA gene-based microarray [ 20 ].” | claim_relations[1] |
| weight | the Baas Becking maxim 'everything is everywhere, but, the environment selects', used to predict that a core exists before it is measured — 0.85 — “If "everything is everywhere, but, the environment selects" [ 6 ], 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.” | claim_relations[2] |
| note | periodontal health —operationalised-as→ no periodontal pockets deeper than 3 mm — '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 paper then quotes the convention it exceeded: 'gingiva is considered healthy if bleeding ... is present at less than 20-25% of gingival sites'. The qualifier in the title rests on a threshold set at half the published bar, and the published bar is itself a range rather than a number — “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 ].” | concept_edges[0] |
| relation | periodontal health —operationalised-as→ no periodontal pockets deeper than 3 mm — health is admitted by two absences - no deep pockets and bleeding at no more than 10% of sites - and the second bar is half the field's stated convention — “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 ].” — “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.” | concept_edges[0] |
| weight | periodontal health —operationalised-as→ no periodontal pockets deeper than 3 mm — 0.95 — “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 ].” | concept_edges[0] |
| note | Phylotypes —operationalised-as→ OTUs clustering sequences at a 3% genetic difference — 'Phylotypes were defined as OTUs clustering sequences at a 3% genetic difference.' Every count in the paper - 818 OTUs, 266 per sample, 500 per mouth - is a count of clusters at one arbitrary width, and the paper reports the same data at 6% and 10% in supplementary files precisely because the number moves — “Phylotypes were defined as OTUs clustering sequences at a 3% genetic difference. The highest taxon (in most cases, genus)” | concept_edges[1] |
| relation | Phylotypes —operationalised-as→ OTUs clustering sequences at a 3% genetic difference — the species-level unit is a clustering threshold on a short region, not a biological determination — “Phylotypes were defined as OTUs clustering sequences at a 3% genetic difference. The highest taxon (in most cases, genus)” — “Phylotypes were defined as OTUs clustering sequences at a 3% genetic difference.” | concept_edges[1] |
| weight | Phylotypes —operationalised-as→ OTUs clustering sequences at a 3% genetic difference — 0.95 — “Phylotypes were defined as OTUs clustering sequences at a 3% genetic difference. The highest taxon (in most cases, genus)” | concept_edges[1] |
| note | approximal surfaces —same-as→ plaque stagnations sites — 'The approximal surfaces, also known as plaque stagnations sites, are protected from regular toothbrushing.' The anatomical name says where it is and the clinical name says what goes wrong there - and the paper uses the second to explain why those sites showed the highest diversity, so the name carries the hypothesis — “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 [ 21 ] was sampled at these sites.” | concept_edges[2] |
| relation | approximal surfaces —same-as→ plaque stagnations sites — one anatomical location with an anatomical name and a clinical name, the second naming what accumulates there — “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 [ 21 ] was sampled at these sites.” — “The approximal surfaces, also known as plaque stagnations sites, are protected from regular toothbrushing.” | concept_edges[2] |
| weight | approximal surfaces —same-as→ plaque stagnations sites — 0.95 — “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 [ 21 ] was sampled at these sites.” | concept_edges[2] |
| effect direction | The stringent selection of sequences —causes→ the depth of pyrosequencing resolution — reduces — “The stringent selection of sequences (the cut-off of 5 reads) and individual labelling and sequencing of 29 samples on a single pyrosequencing plate have largely reduced the depth of pyrosequencing resolution. On average, 10000 reads per sample were obtained instead of the 400000 reads possible when using a full plate for a single sample. Our findings on diversity, therefore, should be considered conservative.” — “have largely reduced the depth of pyrosequencing resolution” | concept_edges[3] |
| note | The stringent selection of sequences —causes→ the depth of pyrosequencing resolution — 'The stringent selection of sequences (the cut-off of 5 reads) and individual labelling and sequencing of 29 samples on a single pyrosequencing plate have largely reduced the depth of pyrosequencing resolution ... Our findings on diversity, therefore, should be considered conservative.' A fortyfold reduction declared in the same paragraph as the filter, alongside 'we have also removed many less frequent but valid sequences' — “The stringent selection of sequences (the cut-off of 5 reads) and individual labelling and sequencing of 29 samples on a single pyrosequencing plate have largely reduced the depth of pyrosequencing resolution. On average, 10000 reads per sample were obtained instead of the 400000 reads possible when using a full plate for a single sample. Our findings on diversity, therefore, should be considered conservative.” | concept_edges[3] |
| relation | The stringent selection of sequences —causes→ the depth of pyrosequencing resolution — the five-read cut-off and 29-way multiplexing cut the reads per sample from a possible 400,000 to about 10,000, and the authors call their diversity findings conservative because of it — “The stringent selection of sequences (the cut-off of 5 reads) and individual labelling and sequencing of 29 samples on a single pyrosequencing plate have largely reduced the depth of pyrosequencing resolution. On average, 10000 reads per sample were obtained instead of the 400000 reads possible when using a full plate for a single sample. Our findings on diversity, therefore, should be considered conservative.” — “The stringent selection of sequences (the cut-off of 5 reads) and individual labelling and sequencing of 29 samples on a single pyrosequencing plate have largely reduced the depth of pyrosequencing resolution.” | concept_edges[3] |
| weight | The stringent selection of sequences —causes→ the depth of pyrosequencing resolution — 0.95 — “The stringent selection of sequences (the cut-off of 5 reads) and individual labelling and sequencing of 29 samples on a single pyrosequencing plate have largely reduced the depth of pyrosequencing resolution. On average, 10000 reads per sample were obtained instead of the 400000 reads possible when using a full plate for a single sample. Our findings on diversity, therefore, should be considered conservative.” | concept_edges[3] |
| audit | a short hypervariable region of the small subunit (SSU) rRNA gene —distinct-from→ the entire SSU rRNA gene — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span says the regions MAY NOT correlate exactly; hedged. — “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.” | concept_edges[4] |
| note | a short hypervariable region of the small subunit (SSU) rRNA gene —distinct-from→ the entire SSU rRNA gene — '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.' The species threshold is applied to a different measurement from the one it was calibrated on, and kept anyway: 'to be consistent with other research ... we have chosen to use 3% OTUs as well' — “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.” | concept_edges[4] |
| relation | a short hypervariable region of the small subunit (SSU) rRNA gene —distinct-from→ the entire SSU rRNA gene — 3% divergence over the sequenced fragment need not equal 3% over the whole gene, and the relationship varies by region and by organism — “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.” — “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.” | concept_edges[4] |
| weight | a short hypervariable region of the small subunit (SSU) rRNA gene —distinct-from→ the entire SSU rRNA gene — 0.95 — “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.” | concept_edges[4] |
| outcome | control — partial — “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.” — “The stringent selection of sequences (the cut-off of 5 reads) and individual labelling and sequencing of 29 samples on a single pyrosequencing plate have largely reduced the depth of pyrosequencing resolution. On average, 10000 reads per sample were obtained instead of the 400000 reads possible when using a full plate for a single sample. Our findings on diversity, therefore, should be considered conservative.” | controls[0] |
| outcome | control — failed — “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.” — “On the other hand, 17-19% of the unique sequences originating from a single oral cavity were not shared with either of the other two microbiomes (Table 3 ).” | controls[1] |
| outcome | control — held — “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.” — “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 ].” | controls[2] |
| limitation | THREE PEOPLE, ALL MALE, ALL CAUCASIAN, TWO FROM ONE COUNTRY: `Three healthy Caucasian male adults ... with no antibiotic use in the past three months` - and the conclusion generalises to `unrelated healthy individuals`. The exclusion list is long and precise (`no clinical signs of oral mucosal disease ... did not suffer from halitosis, did not have caries ... or periodontal disease`) and the inclusion population is three — “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.” | limitations[0] |
| limitation | AND 17-19% OF EACH PERSON'S SEQUENCES ARE SHARED WITH NEITHER OTHER PERSON: `On the other hand, 17-19% of the unique sequences originating from a single oral cavity were not shared with either of the other two microbiomes.` The core is 66% of reads and the exclusive fraction is a fifth of sequences - both true, and only the first is in the title — “On the other hand, 17-19% of the unique sequences originating from a single oral cavity were not shared with either of the other two microbiomes (Table 3 ).” | limitations[1] |
| limitation | AND THE DIVERSITY FIGURES ARE AN ORDER OF MAGNITUDE ABOVE THE METHODS THEY REPLACE: `each individual sample harboured 266 `SPECIES-LEVEL` phylotypes (SD 67; range 123 - 326) ... considerably higher than the previously reported 4 - 28 species per site using traditional cloning and sequencing methods or 10 - 81 species using a 16S rRNA gene-based microarray.` A tenfold to fiftyfold change in a count, attributable to the instrument rather than to the mouths — “On average, each individual sample harboured 266 "species-level" phylotypes (SD 67; range 123 - 326) (Figure 6A ). This is again considerably higher than the previously reported 4 - 28 species per site using traditional cloning and sequencing methods [ 15 ] or 10 - 81 species using a 16S rRNA gene-based microarray [ 20 ].” | limitations[2] |
| limitation | AND THE MOTIVATING CLAIM IS THAT HEALTH IS THE UNDER-DESCRIBED STATE: `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.` A definition of normal proposed because the abnormal was defined first — “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.” | limitations[3] |
finding · quantity · variable · definition · key-term — grounded in the full text| finding | — “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.” | content_tags.findings[0] |
| finding | — “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.” | content_tags.findings[1] |
| finding | — “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.” | content_tags.findings[2] |
| finding | — “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.” | content_tags.findings[3] |
| finding | — “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.” | content_tags.findings[4] |
| finding | — “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 ].” | content_tags.findings[5] |
| finding | — “On the other hand, 17-19% of the unique sequences originating from a single oral cavity were not shared with either of the other two microbiomes (Table 3 ).” | content_tags.findings[6] |
| quantity | 1,660 of 6,315 unique sequences, contributing 66% of reads — — “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.” | content_tags.quantities[0] |
| quantity | 65% of unique sequences, 75% of OTUs, 84% of higher taxa; 66%, 94% and 99.8% of reads respectively — — “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.” | content_tags.quantities[1] |
| quantity | about 10,000 instead of the 400,000 a full plate allows — — “The stringent selection of sequences (the cut-off of 5 reads) and individual labelling and sequencing of 29 samples on a single pyrosequencing plate have largely reduced the depth of pyrosequencing resolution. On average, 10000 reads per sample were obtained instead of the 400000 reads possible when using a full plate for a single sample. Our findings on diversity, therefore, should be considered conservative.” | content_tags.quantities[2] |
| quantity | ~25,000 phylotypes estimated from ~100 pooled microbiomes — — “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 [ 4 ]. This is an order of magnitude higher than previously reported 700 oral microbial phylotypes as identified by cultivation or traditional cloning and sequencing [ 5 ]. 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 [ 4 ]. If "everything is everywhere, but, the environment selects" [ 6 ], 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 [ 4 ] actually contribute to a single oral cavity and how common or exclusive individual oral microbiomes of unrelated healthy individuals are.” | content_tags.quantities[3] |
| variable | abundance — “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".” | content_tags.variables[0] |
| definition | core microbiome — “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.” | content_tags.definitions[0] |
| definition | phylotype — “Phylotypes were defined as OTUs clustering sequences at a 3% genetic difference. The highest taxon (in most cases, genus)” | content_tags.definitions[1] |
| concept | periodontal health — not defined in this paper's own words; the inclusion criterion that makes a volunteer count as healthy here — “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 ].” | concepts[0] |
| concept | no periodontal pockets deeper than 3 mm — not defined in this paper's own words; half of the operational definition of health in this study — “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 ].” | concepts[1] |
| concept | Phylotypes — not defined in this paper's own words; the species-level unit this paper counts in — “Phylotypes were defined as OTUs clustering sequences at a 3% genetic difference. The highest taxon (in most cases, genus)” | concepts[2] |
| concept | OTUs clustering sequences at a 3% genetic difference — not defined in this paper's own words; the operational stand-in for a species, adopted for comparability — “Phylotypes were defined as OTUs clustering sequences at a 3% genetic difference. The highest taxon (in most cases, genus)” | concepts[3] |
| concept | approximal surfaces — not defined in this paper's own words; the tooth surfaces between adjacent teeth, protected from brushing — “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 [ 21 ] was sampled at these sites.” | concepts[4] |
| concept | plaque stagnations sites — not defined in this paper's own words; the same surfaces under the clinical name — “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 [ 21 ] was sampled at these sites.” | concepts[5] |
| concept | The stringent selection of sequences — not defined in this paper's own words; the five-read cut-off plus 29-sample multiplexing on one plate — “The stringent selection of sequences (the cut-off of 5 reads) and individual labelling and sequencing of 29 samples on a single pyrosequencing plate have largely reduced the depth of pyrosequencing resolution. On average, 10000 reads per sample were obtained instead of the 400000 reads possible when using a full plate for a single sample. Our findings on diversity, therefore, should be considered conservative.” | concepts[6] |
| concept | the depth of pyrosequencing resolution — not defined in this paper's own words; 10,000 reads per sample instead of the 400,000 a full plate allows — “The stringent selection of sequences (the cut-off of 5 reads) and individual labelling and sequencing of 29 samples on a single pyrosequencing plate have largely reduced the depth of pyrosequencing resolution. On average, 10000 reads per sample were obtained instead of the 400000 reads possible when using a full plate for a single sample. Our findings on diversity, therefore, should be considered conservative.” | concepts[7] |
| concept | a short hypervariable region of the small subunit (SSU) rRNA gene — not defined in this paper's own words; the V5-V6 stretch actually sequenced, over which the 3% threshold is applied — “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.” | concepts[8] |
| concept | the entire SSU rRNA gene — not defined in this paper's own words; the whole gene the 3% convention was established on — “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.” | concepts[9] |
| a diversity index and a taxon count disagreeing on the same sample | — “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 [ 22 ] 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.” | content_tags.statistics[0] |
Full text — tagged
The reference list is not part of the text we hold for this paper. Our copy was extracted from the publisher's page and the bibliography did not survive that extraction, so the article below ends before its references. Nothing has been removed by us.
44 bmcmicr BMC Microbiology BMC Microbiol BMC PMC2805672 2805672 2805672 20003481 10.1186/1471-2180-9-259 Defining the healthy "core microbiometerm·def" of oral microbial communities ZaurapersName Egija 1 ✉ KeijserpersName Bart JF 2 HusepersName Susan M 3 CrielaardpersName Wim 1 1 Department of Cariology, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Free University Amsterdam, the Netherlands 2 TNO Quality of Life, Business Unit Food and Biotechnology Innovations, Microbial Genomics Group, Zeist, the Netherlands 3 Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory, Woods Hole, MA, USA ✉ Corresponding author.
15 12 2009 9 259 259 13 1 2010 Copyright ©2009 ZaurapersName et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background 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).
Results 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" phylotypesterm (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" phylotypesterm (SD 67; range 123 - 326) with cheek samples being the least diverse and the dental samples from approximal surfacesterm 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" phylotypesterm 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 microbiometerm·def") 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.
Conclusions 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 microbiometerm·def at health.
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no Received 2009 Jul 10; Accepted 2009 Dec 15; Collection date 2009.
Background The commensal human microbiome is estimated to outnumber the amount of human body cells by a factor of ten [ 1 ]. 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 [ 2 ]. 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 [ 3 ]. 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 phylotypesterm [ 4 ]. This is an order of magnitude higher than previously reported 700 oral microbial phylotypesterm as identified by cultivation or traditional cloning and sequencing [ 5 ]. 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 phylotypesterm [ 4 ].
If "everything is everywhere, but, the environment selects" [ 6 ], then a healthy oral microbiome should be dominated by a "core microbiometerm·def" characteristic for health. These abundant phylotypesterm 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 phylotypesterm [ 4 ] 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 [ 7 ]. Approximal (adjoining) surfaces between adjacent teeth have limited access to fluorides and saliva, and therefore have a predilection for dental caries [ 8 ]. 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.
The final data contained 298261 reads and resulted in 6315 unique sequences (Table 1 , Table 2 ). The average length of sequence reads was 241 nt. The stringent selection of sequencesterm (the cut-off of 5 reads) and individual labelling and sequencing of 29 samples on a single pyrosequencing plate have largely reduced the depth of pyrosequencing resolutionterm. On average, 10000 reads per sample were obtained instead of the 400000 reads possible when using a full plate for a single sample. Our findings on diversity, therefore, should be considered conservative.
Table 1 Participant details and number of sequences, OTUs and higher taxa.
Individual, Age Birth Country All Reads Reads Analyzed a Unique Sequences OTUs at 3% Difference b OTUs at 6% Difference b OTUs at 10% Difference b Higher Taxa c S1, 39 The Netherlands 154530 100226 4124 630 418 269 95 S2, 29 Brazil 132649 86224 3668 541 370 237 88 S3, 45 The Netherlands 164892 111811 4293 649 434 282 104 a Only reads that were observed five or more times were included in the analyses.
b Sequences were clustered into Operational Taxonomic Units (OTUs) at 3%, 6% or 10% genetic difference.
c Higher taxa refers to genus or to a more inclusive taxon (family, order, class) when sequence could not be confidently classified to the genus level.
Table 2 Distribution of reads, unique sequences, OTUs and shared microbiome (sequences and OTUs) per phylum.
Phylum Number of Reads (% of all) a Unique Sequences (% of all) a Number of Shared Sequences b % of Reads with Shared Sequences Number of OTUs (% of all) c Number of Shared OTUs d % of Reads with Shared OTUs Actinobacteria 73092 (25%) 1541 (24%) 520 20% 194 (24%) 94 24% Bacteroidetes 32666 (11%) 748 (12%) 118 6% 132 (16%) 44 9% Cyanobacteria 28 (0.01%) 4 (0.06%) 1 0.005% 3 (0.4%) 1 0.006% Firmicutes 107711 (36%) 2283 (36%) 719 27% 230 (28%) 131 35% Fusobacteria 14103 (5%) 233 (4%) 74 3% 37 (5%) 23 4% Proteobacteria 65778 (22%) 1294 (20%) 212 12% 183 (22%) 77 20% Spirochaetes 407 (0.1%) 18 (0.3%) 2 0.06% 8 (1%) 2 0.1% TM7 3853 (1%) 127 (2%) 13 0.4% 14 (2%) 7 0.8% Unclassified Bacteria 623 (0.2%) 67 (1%) 1 0.002% 17 (2%) 8 0.1% Total 298261 (100%) 6315 (100%) 1660 66% 818 (100%) 387 93% a - sum of all samples of three individuals; only sequences that were present at least 5 times are included b - number of unique sequences that were common in all three microbiomes c - number of all OTUs (sequences that clustered at 3% genetic difference) d - number of OTUs (sequences clustered at 3% genetic difference) that were common in all three microbiomes Clustering of the overall data in phylotypesterm 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" phylotypesterm [ 1 , 11 , 12 ]. A 3% variation within a short hypervariable region of the small subunit (SSU) rRNA geneterm may not correlate exactly with a 3% variation along the entire SSU rRNA geneterm. 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 ).
The size of the OTUs (number of reads per OTU) correlated significantly (p < 0.001; Spearman's rho 0.930) with the number of unique sequences within an OTU (Figure 1 ), i.e ., the most abundant OTUs harboured the highest counts of unique sequences. An obvious outlier was one abundant OTU (0.9% of all reads), classified as Fusobacterium which contained only three unique sequences. Six other abundant OTUs (1.4 - 6.7% of all reads) contained more than 140 (range 145 - 265) unique sequences each. Four of these OTUs were assigned to the genus Streptococcus (OTU ID 803; 165; 230; 262), one to the genus Corynebacterium (ID 145), and one to the genus Neisseria (ID 637). Two-thirds of all OTUs contained a single sequence; however these were low abundanceterm·var OTUs (5 - 49 reads), together contributing to just 0.7% of all reads (Figure 1 , Additional file 1 ).
Figure 1 The size of OTU clusters and the number of unique sequences per cluster . The number of reads within each OTU (sequences that clustered at 3% genetic distance level) and the number of unique sequences per OTU are plotted in the rank order of OTU cluster size (high to low).
Diversity and taxonomy of individual microbiomes Within an individual oral cavity, over 3600 sequences comprising over 500 "species-level" phylotypesterm (Figure 2 ) and 88 - 104 higher taxa (genus level or above) were found (Table 1 , Additional file 4 ). This richness is considerably higher than the 34 to 72 phylotypesterm and the 6 to 30 genera previously described using conventional cloning and sequencing [ 15 , 16 ]. 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 ) (Additional file 4 ).
Figure 2 The relative abundanceterm·var of OTUs per individual . Relative abundanceterm·var of OTUs based on all unique sequences (0%, solid lines) and OTUs within genetic distances that do not exceed 3% difference (3%, dashed lines) per individual S1, S2 and S3, respectively. The x-axis indicates the individual OTUs, ranked according to their relative abundanceterm·var (high to low). The y-axis indicates the cumulative abundanceterm·var of the OTUs.
About 100 "species-level" phylotypesterm (118, 97 and 112 phylotypesterm in the microbiome of individual S1, S2 and S3, respectively) belonged to abundant OTUs of the individual microbiome (Additional file 1 ). A phylotypeterm·def was considered abundant if it contributed to at least 0.1% of the microbiome. These abundant phylotypesterm together contributed to 92 - 93% of each microbiome.
As with a pooled oral microbiome [ 4 ] and individually sequenced gut microbiomes [ 13 ], 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 Unique sequences Twenty-six percent (1660 sequences) of the unique sequences were found in all three microbiomes and 65% in at least two microbiomes (Figure 3A ). Of all reads, 66% belonged to sequences that were shared by three microbiomes (Table 2 ). Nine sequences were highly abundant (0.5 - 5.8% of the reads) across all individuals: they contributed to 11%, 9% and 21% of the microbiome of individuals S1, S2 and S3, respectively (the full list of the taxonomy and abundanceterm·var of the overlapping sequences is given in Additional file 5 ). Two of these sequences were assigned to the genus Streptococcus , two to the family Veillonellaceae , one each to the genera Granulicatella (Firmicutes), Corynebacterium , Rothia (Actinobacteria), Porphyromonas (Bacteroidetes) and Fusobacterium (Fusobacteria).
Figure 3 The extent of overlap of oral microbiome between three individuals . The extent of overlap between subjects S1 (pink circle), S2 (light blue circle) and S3 (yellow circle) at the level of A) unique sequences, B) OTUs clustered at 3% difference and C) higher taxa (genus or more inclusive taxon). The data was obtained by combining all samples of the respective individual microbiome. The Venn Diagrams show that 26% of the unique sequences, 47% of the OTUs and 72% of the higher taxa were common (area in grey) to the three individuals.
On the other hand, 17-19% of the unique sequences originating from a single oral cavity were not shared with either of the other two microbiomes (Table 3 ). Combined, these "exclusive" sequences contributed to 11 - 20% of the total count of reads within an individual microbiome. Within an individual, one to six "exclusive" sequences were highly abundant (Table 3 ). Sequencing of a larger number of individual microbiomes is necessary for assessing the true exclusivity of these abundant individual-specific sequences.
Table 3 Relative abundanceterm·var of individual-specific ("exclusive") sequences Individual % Sequences "Exclusive" % of Reads with "Exclusive" Sequences Taxonomy of Predominant "Exclusive" Sequences a % of Reads Nr of Samples b S1 19 20 Firmicutes;Bacilli; Lactobacillales;Streptococcaceae;Streptococcus 4.4 3 Bacteria;Bacteroidetes; Bacteroidia;Bacteroidales 1.2 9 Bacteria;Proteobacteria;Gammaproteobacteria; Pasteurellales;Pasteurellaceae 1.2 8 Bacteria;Proteobacteria;Gammaproteobacteria; Pasteurellales;Pasteurellaceae;Haemophilus 0.6 4 Bacteria;Proteobacteria;Gammaproteobacteria; Pasteurellales;Pasteurellaceae 0.6 5 Bacteria;Proteobacteria;Gammaproteobacteria; Cardiobacteriales;Cardiobacteriaceae;Cardiobacterium 0.5 4 S2 19 12 Bacteria;Proteobacteria;Betaproteobacteria; Neisseriales;Neisseriaceae;Neisseria 0.6 3 S3 17 11 Bacteria;TM7 0.7 3 Bacteria;Firmicutes;Bacilli; Bacillales;Staphylococcaceae;Gemella 0.5 7 Bacteria;Actinobacteria;Actinobacteria; Actinomycetales;Corynebacteriaceae;Corynebacterium 0.5 5 a - sequence was considered predominant if it contributed to at least 0.5% of the individual microbiome b - number of samples of the particular individual where the respective "exclusive" sequence was found Phylotypesterm All three microbiomes shared 387 (47%) of 818 OTUs (Figure 3B ). These overlapping phylotypesterm together contributed to 90 - 93% of each microbiome (Additional file 1 ). Fifty-one of these shared OTUs were abundant (≥0.1% of microbiome) and together occupied 62 - 73% of the individual microbiome (Figure 4 ).
Figure 4 Shared abundant phylotypesterm in three oral microbiomes and their relative abundanceterm·var . Relative abundanceterm·var of shared phylotypesterm within an individual microbiome. Only abundant phylotypesterm that contributed to at least 0.1% of the individual microbiome are shown. The most abundant phylotypesterm (≥0.5% of the microbiome) are grouped separately in the upper panel. Phylotypesterm were defined as OTUs clustering sequences at a 3% genetic differenceterm. The highest taxon (in most cases, genus) at which the OTU was identified, is shown together with the cluster identification number. The full list of OTUs is available in Additional file 1 . Different colours indicate three different microbiomes, S1, S2 and S3, respectively.
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 abundanceterm·var, while in S1 four of 69 unique phylotypesterm were relatively abundant (≥ 0.1% of the microbiome). One phylotypeterm·def (OTU ID 774, Pasteurellaceae ) contributed to 2.2% of this microbiome and was preferentially found around the molar tooth (buccal, lingual and approximal surfacesterm 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 abundanceterm·var of this phylum in the different samples. Since a high prevalence of Spirochaetes in dental plaque is associated with periodontal disease [ 17 ], it would be interesting to assess the degree of similarity and diversity of these phylotypesterm in a group of periodontitis patients.
Higher taxa 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 abundanceterm·var (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 phylotypesterm is unexpectedly high and considerably higher than the recently reported average of 13% similarity in phylotypesterm between any two hands from unrelated individuals [ 12 ]. Of even greater contrast to our findings are the comparisons of gut microbiomes which show no overlap in microbiota in unrelated individuals [ 1 ]. Instead of a core microbiometerm·def at an organismal lineage level, gut microbiomes harboured distinct core genes [ 1 ]. 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 phylotypesterm were shared across the 102 hands sampled [ 12 ]. 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 [ 18 ], and is mostly limited to microorganisms in food or drinking water at a later age.
Sample-specific profiles within individual oral microbiomes Even at the phylum level, distinct differences among various intraoral sites were observed, e.g . Firmicutes dominated the cheek mucosa of volunteers S1 and S3, while the relatively minor phylum, candidate division TM7, was overrepresented at the approximal sites of volunteer S1 and on incisor buccal and incisor approximal surfacesterm of volunteer S3 (Figure 5 ).
Figure 5 Average and site-specific relative distribution of bacterial phyla in three individuals . Average and site-specific relative distribution of bacterial phyla in three individuals (S1, S2 and S3). Unclassified bacteria were reads without a recognizable match in the full 16S rRNA reference database. Sample legend: B - buccal, L - lingual, Appr - approximal surfaceterm of either an incisor (a front tooth) or a molar tooth.
Fifteen taxa were found at all sites in all three individuals: the genera 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 [ 19 ].
Within an individual oral cavity, 36 - 51% of the unique sequences were found solely in a single sample and mostly at a low abundanceterm·var. 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 abundanceterm·var 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 [ 15 ].
On average, each individual sample harboured 266 "species-level" phylotypesterm (SD 67; range 123 - 326) (Figure 6A ). This is again considerably higher than the previously reported 4 - 28 species per site using traditional cloning and sequencing methods [ 15 ] or 10 - 81 species using a 16S rRNA gene-based microarray [ 20 ].
Figure 6 Diversity statistics of individual samples . Diversity statistics: A) number of taxa (OTUs clustering sequences at a 3% genetic differenceterm) per sampling site for each individual; B) diversity index - Shannon diversity index, H, taking into account the number and the proportion (abundanceterm·var) of taxa.
A trend for a higher diversity was observed in the samples taken at the approximal surfacesterm and the lingual surface of the front teeth (Figure 6B ). The approximal surfacesterm, also known as plaque stagnations sitesterm, 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 [ 21 ] 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 [ 22 ] and might explain high dominance of these phylotypesterm in these particular samples. Notably, the cheek sample from S3 still contained one of the highest counts of taxa (234 phylotypesterm), but obviously at a very low abundanceterm·var.
Dimensional reduction of the OTU data by principal component analysis (PCA) explained 51% of the total variance among the individual samples by the first three components (Figure 7A-B ; PCA loadings and respective taxa are listed in Additional file 7 ). The greatest component (PC1, 29.7% of variance) discriminated between the samples of dental and mucosal origin, especially in individuals S1 and S3. The second greatest component (PC2, 12.3% of variance) discriminated all samples of volunteer S3 from the samples of S1 and S2. The third component (PC3, 9.1% of variance) increased the separation of the samples of mucosal and dental origin, e.g . all three tongue samples aligning in the vicinity of each other (Figure 7B ), supporting the earlier findings that the tongue has a specific microbial profile [ 20 ]. Since saliva is easily and non-invasively accessible it is a popular sample in oral epidemiology and microbiome diversity [ 4 , 16 ] studies. In our study, the profiles of the saliva samples were closer to communities obtained from mucosal than dental sites, which is in line with the results of a large scale survey on 225 healthy subjects where 40 selected bacterial species were followed using DNA-DNA hybridization technique [ 23 ].
Figure 7 Principal Component Analysis results on individual samples . Principal Component Analysis (PCA) results on all individual samples at the level of OTUs clustering sequences at a 3% difference: A) the plot of the PCA axis 1 (accounting for 29.7% of intersample variation) and the axis 2 (12.3% of intersample variation); B) the plot of the PCA axis 1 and the axis 3 (9.1% of intersample variation). Blue dots - samples from individual S1, green dots - samples from individual S2, red dots - individual S3. A - approximal, B - buccal, L - lingual surface of i - incisor or m - molar tooth, respectively. Data were normalized to an equal number of reads per sample and log2 transformed.
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 surfaceterm 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 surfacesterm of the two regions of the oral cavity.
Conclusions 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.
Methods Samples 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 healthterm was defined as no periodontal pockets deeper than 3 mmterm 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 surfacesterm of teeth 11/12) and around an upper molar (tooth 16 buccally, lingually, and approximal surfacesterm 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 surfacesterm (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 surfacesterm. 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 [ 26 ] 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.
Molecular techniques 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 MgCl 2 , 200 μM dNTP PurePeak DNA polymerase Mix (Pierce Nucleic Acid Technologies, Milwaukee, WI), 1.5 mM MgSO 4 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].
Data analysis GS-FLX sequencing data were processed as previously described [ 14 ]. 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 [ 27 ] 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 [ 28 ]. 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 [ 29 ]. 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 [ 14 ] and using DOTUR [ 30 ] 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 abundanceterm·var 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 abundanceterm·var 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 [ 31 ]. 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).
Authors' contributions EZ and WC have contributed to the design of the clinical study; EZ carried out clinical procedures; BJFK processed the samples; SMH performed sequence analyses; EZ, BJFK, SMH and WC drafted the manuscript. All authors read and approved the final manuscript.
Supplementary Material Additional file 1 Full list and taxonomy of OTUs clustered at 3% difference in descending order of their relative abundanceterm·var (%) . This is an Excel file listing all 818 OTUs, number of unique sequences within each OTU, abundanceterm·var and the taxonomic assignment of each OTU per individual S1, S2 and S3.
Click here for file Additional file 2 Full list and taxonomy of OTUs clustered at 6% difference in descending order of their relative abundanceterm·var (%) . This is an Excel file listing all 517 OTUs, abundanceterm·var and the taxonomic assignment of each OTU per individual S1, S2 and S3.
Click here for file Additional file 3 Full list and taxonomy of OTUs clustered at 10% difference in descending order of their relative abundanceterm·var (%) . This is an Excel file listing all 320 OTUs, abundanceterm·var and the taxonomic assignment of each OTU per individual S1, S2 and S3.
Click here for file Additional file 4 Full list and relative abundanceterm·var of higher taxa per individual microbiome . This is an Excel file listing all 112 higher taxa (genera or more inclusive taxa when sequences could not be confidently classified to the genus level) and their relative abundanceterm·var in oral microbiomes of three individuals: S1, S2 and S3.
Click here for file Additional file 5 Relative abundanceterm·var of 1660 unique sequences that were shared by three individuals (S1, S2 and S3) . This Excel file lists the taxonomy of the sequences shared by three individuals, ranked by the abundanceterm·var of these sequences in the total data set. The sequences are available at the Short Read Archive of NCBI as SRP000913.
Click here for file Additional file 6 Full list and absolute abundanceterm·var of higher taxa per individual sampling site . This is an Excel file listing all 112 higher taxa (genera or more inclusive taxa when sequences could not be confidently classified to the genus level) and their abundanceterm·var in 29 samples from three individuals: S1, S2 and S3. Data were not normalized.
Click here for file Additional file 7 Full list of taxa and PCA loadings . This is an Excel file listing the loadings of the first three components of the Principal Component Analysis (PCA) on all 818 OTUs (3% genetic difference) and all 29 samples (the corresponding PCA plots are shown in Figure 7 ). The loadings marked in bold and highlighted are above the arbitrary significance threshold of 1 or -1. The positive values are highlighted yellow; the negative values are highlighted turquoise.
Click here for file Contributor Information Egija ZaurapersName, Email: e.zaurapersName@acta.nl.
Bart JF KeijserpersName, Email: bart.keijserpersName@tno.nl.
Susan M HusepersName, Email: shuse@mbl.edu.
Wim CrielaardpersName, Email: w.crielaardpersName@acta.nl.
Acknowledgements 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 HusepersName 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.