The composition of the microbial communities on and within the human body varies between individuals. Inter-individual variation has been demonstrated in a variety of studies for the healthy intestinal tract (Eckburg et al., 2005
; Dethlefsen et al., 2006
; Ley et al., 2006
; Palmer et al., 2007
). In contrast, knowledge about the inter-individual differences in the healthy human mouth microbiota and the uniqueness of the oral microbiota compared to other microbial communities in our bodies is still somewhat sparse. Several molecular studies have been conducted regarding the composition of the oral microbiota, but these studies used limited numbers of sequences per individual, or only looked at short regions of the 16S rRNA gene (Kroes et al., 1999
; Paster et al., 2001
; Kazor et al., 2003
; Aas et al., 2005
). A study by Diaz et al.
in three individuals showed that early colonization of enamel is subject-specific (Diaz et al., 2006
). The distinctness of the phylogenetic structure of the human oral microbiota in relation to the microbiota of the skin and feces in nine individuals was revealed in a recent study (Costello et al., 2009
). While other studies have considered the oral microbiota of a larger number of individuals, our study was based on one of the largest sets of near full-length sequences per individual to date for the human oral cavity. The most important contributions of this work are the combination of depth of coverage and degree of phylogenetic resolution for the human mouth, the features of a human oral core microbiota, and previously-unrecognized patterns of taxon co-occurrence.
In this study, we amplified and analyzed an average number of 1029 near full-length, well-aligned oral 16S rRNA gene sequences (range 931 – 1070) per subject from each of 10 healthy individuals, as well as an additional 1083 clones from the pooled subgingival specimens, bringing the total number of sequences analyzed in this study to 11 368. The advantage of near full-length 16S rRNA gene sequences in providing greater phylogenetic resolution than hypervariable region “tag” sequences was highlighted in a comparative analysis of these two types of sequence data (Huse et al., 2008
). In this dataset, we identified a total of 247 different OTUs at the level of species, of which 24 were less than 99% identical than previously published sequences. Approximately 10% of the OTUs found in this study were previously uncharacterized.
The abundant bacterial groups found in our study are similar to those found in most other studies. For example, 20% of our sequences belonged to the genus Streptococcus
, confirming the preponderance of Streptococcus
species within the healthy mouth by microscopy and culture (Socransky, 1963
) and by molecular methods (Kroes et al., 1999
). In a recent molecular study, the most predominant bacterial genera in the oral cavity were Streptococcus, Gemella, Abiotrophia, Granulicatella, Rothia, Neisseria,
(Aas et al., 2005
). We found those same groups to be prevalent as well, but, in addition, we found many Proteobacteria (e.g., Haemophilus, Lautropia
) to be abundant. This difference may be the result of a deeper sequencing effort per individual in the current study (average 57.5 clones per subject in the Aas et al
. study for a total of 2,589 clones, in contrast to an average 1029 clones per individual in this study). In addition, different DNA extraction methods and different broad-range PCR primers could also explain the divergent results.
Despite the evidence for a conserved healthy oral community at the genus level in all 10 healthy mouths, there was also evidence in this study for large inter-individual differences. Our study confirms results by Nasidze et al.
suggesting high variability in the oral microbiome between individuals, although in the latter study, saliva was the only specimen type examined (Nasidze et al., 2009
). In addition to Streptococcus,
which was the most abundant genus in the combined dataset and in three of the individual mouths, we identified four additional genera that may dominate the oral ecosystem of a healthy subject. Our data indicate that there is a variety of alternative oral bacterial community structures, and a greater degree of variation in patterns of diversity, associated with oral health than previously thought. It remains to be seen what factors, e.g., human genetics or lifestyle, correlate with oral bacterial community structure. Clearly, the concept of a core oral microbiome may be better defined with measurements of community function, rather than community membership. Such analyses will need to include community-wide assessments of gene content, gene transcript abundance, and protein products.
The role of bacteria in periodontal disease is complex, and likely involves polymicrobial consortia (Lepp et al., 2004
). Socransky and Haffajee have proposed that the presence of a high proportion of so-called “red complex” bacteria, i.e., Porphyromonas gingivalis
, Tannerella forsythia,
and Treponema denticola,
is associated with periodontal disease (Haffajee et al., 2008
; Socransky et al., 1998
). In a survey of five healthy mouths, Aas et al.
(Aas et al., 2005
) did not find any representatives of the “red complex”. Other studies have, however, identified members of this complex in healthy mouths (Ximenez-Fyvie et al., 2000
). In our study, all three species were found in subjects with healthy gingival tissues, albeit in low numbers, and limited to subjects 1, 4, and 9. Taken together with previous studies, this study confirms that the ‘red complex’ group may be found in small numbers in healthy individuals. Other bacterial species such as Filifactor alocis
species, and Dialister
species have been associated with a worsening periodontal status (Kumar et al., 2005
). A bacterial species previously shown to be associated with periodontal health (Veillonella parvula
X042, Genbank accession number AF287781) (Kumar et al., 2005
) was found in all specimens in this study, and was the third most abundant OTU in our combined sequence dataset.
UniFrac PCA analysis showed no apparent clustering of oral microbial communities based on gender, age, or ethnicity. In addition, UniFrac analysis showed no apparent effect of DNA extraction method of oral specimens. No individual pool was found to be more significantly different than others in pairwise comparisons, and the subgingival library was not significantly different from the individual pools. This may be indicative of the fact that (1) despite the many different habitats in the human mouth, many bacterial species are shared among those habitats, or (2) that the individual pools are dominated by the subgingival specimens. However, the number of subjects in this study was relatively small, and inter-individual differences associated with gender, age, or ethnicity might become apparent when larger numbers of subjects are studied. Because specimens from multiple sites within an individual were pooled, bacterial community differences between anatomical sites could not be examined.
When the oral sequence libraries were compared to similar sequence libraries from the human colon and stool, a clear clustering according to anatomical site was observed. These results need to be interpreted with caution, since data were obtained from different individuals, and differences between study groups might drive some of the findings. But it is appealing to assume that each anatomical location within a healthy human has specific physiochemical conditions that shape the composition of a microbial community specifically adapted to that site. Our finding of human habitat-specific microbial community structure is supported by recently published data (Costello et al., 2009
Tests for significant segregation patterns of taxa were originally developed as a means of assessing whether competition between taxa is a driving force behind community assembly. C-scores higher than expected are consistent with inter-species competition, as well as with habitat differences that cross over the sampling scheme, and historical processes. We feel that habitat differences (other than host genotype) were minimized in our study due to the fact that the pools presumably represented multiple intra-oral sites in a consistent manner across individuals. However, successional or early historical differences between subjects cannot be eliminated as a possible explanation of the observed segregation patterns. It has been previously suggested that as taxonomic level is refined, C-scores become more statistically significant (Horner-Devine et al., 2007
). The fact that significant segregation was found at the genus level in our study but not at a level equivalent to species has several possible interpretations. One possibility is that taxonomic levels are not the relevant biological units of measure. Another possibility is that the level of ecological interest and interaction in the mouth is the level that humans have chosen to label as genus, rather than species.
Co-occurrence analysis not only addresses the forces structuring a community, but also draws attention to specific taxa that have apparent interactions and may be worthy of further investigation. For instance, in this study, Abiotrophia
was found to have a high number of checkerboard units with the genera Dialister
, and the genus Scardovia
had a high number of checkerboard units with Eikenella
. Interactions among these genera have not been the focus of research so far, but such research may lead us to understand whether and why these taxa compete. Each of these genera (except Treponema
) is represented in this dataset by a single species, each of which has been implicated in human disease; recognition of competitive partners may prove useful in preventive medicine. For instance, it has been suggested that known competitive interactions between Streptococcus mutans
and other species may be exploited in order to develop preventive treatments for dental caries, by encouraging growth of species with lower cariogenicity (Kreth et al., 2005
This study demonstrates that each person’s mouth harbors a unique community of bacterial species, but that these communities tend to be more similar when classified at the level of genus. Ecological tools initially developed for larger organisms, such as co-occurrence analysis, will greatly facilitate the analysis of complex bacterial communities such as those found in the human body, and will enhance our understanding of the role of the microbiota in health and disease.