Preliminary characterization of the oral microbiota of Chinese adults with and without gingivitis.

BACKGROUND: Microbial communities inhabiting human mouth are associated with oral health and disease. Previous studies have indicated the general prevalence of adult gingivitis in China to be high. The aim of this study was to characterize in depth the oral microbiota of Chinese adults with or without gingivitis, by defining the microbial phylogenetic diversity and community-structure using highly paralleled pyrosequencing.
METHODS: Six non-smoking Chinese, three with and three without gingivitis (age range 21-39 years, 4 females and 2 males) were enrolled in the present cross-sectional study. Gingival parameters of inflammation and bleeding on probing were characterized by a clinician using the Mazza Gingival Index (MGI). Plaque (sampled separately from four different oral sites) and salivary samples were obtained from each subject. Sequences and relative abundance of the bacterial 16 S rDNA PCR-amplicons were determined via pyrosequencing that produced 400 bp-long reads. The sequence data were analyzed via a computational pipeline customized for human oral microbiome analyses. Furthermore, the relative abundances of selected microbial groups were validated using quantitative PCR.
RESULTS: The oral microbiomes from gingivitis and healthy subjects could be distinguished based on the distinct community structures of plaque microbiomes, but not the salivary microbiomes. Contributions of community members to community structure divergence were statistically accessed at the phylum, genus and species-like levels. Eight predominant taxa were found associated with gingivitis: TM7, Leptotrichia, Selenomonas, Streptococcus, Veillonella, Prevotella, Lautropia, and Haemophilus. Furthermore, 98 species-level OTUs were identified to be gingivitis-associated, which provided microbial features of gingivitis at a species resolution. Finally, for the two selected genera Streptococcus and Fusobacterium, Real-Time PCR based quantification of relative bacterial abundance validated the pyrosequencing-based results.
CONCLUSIONS: This methods study suggests that oral samples from this patient population of gingivitis can be characterized via plaque microbiome by pyrosequencing the 16 S rDNA genes. Further studies that characterize serial samples from subjects (longitudinal study design) with a larger population size may provide insight into the temporal and ecological features of oral microbial communities in clinically-defined states of gingivitis.

Background

Metagenomic techniques have recently revolutionized our understanding of the plethora of microbes that co-inhabit the human body, collectively known as the human microbiome. Various body sites (e.g. the skin, the gastrointestinal and vaginal tracts and the oral cavity) harbor distinct communities of microbes that vary among host individuals as well as among the ecological niches within each body site [1]. Interactions among resident microbiota and between the microbiota and the human host underlie human health and disease. Within the oral cavity, the tongue, soft and hard palates, buccal mucosa, supragingival and subgingival surfaces of the teeth and saliva may represent different ecological niches or habitats [2]. The composition and diversity of microbiota in these habitats may contribute to oral health [3-6] and oral diseases such as dental caries, periodontitis, and gingivitis [7,8].

Gingivitis is inflammation of the soft tissues of the gum surrounding the teeth. It is believed to result from the build-up of plaque [9] and the ensuing interactions between the plaque microbiota and host tissues [10,11]. These tissues become erythematous and bleed upon probing, but no apical migration of the junctional epithelium occurs. Previous studies of gingival plaque showed that as gingivitis develops, the microbial constituents of subgingival plaque shift from a population dominated by Gram-positive streptococci to one with elevated levels of Gram-negative anaerobes such as Actinobacillus, Capnocytophaga, Campylobacter, Eikenella, Fusobactrium and Prevotella [2,12,13]. However, these studies have been based on culture-based and molecular methods that target only a limited and partial number of culturable microbes, a bias that can be overcome by metagenomic approaches. During the last decade, high-throughput sequencing approaches based on 16 S rDNA amplicons have been used to survey the diversity of human oral microbiota in health and disease. Notably, these techniques revealed the microbial diversity within the healthy subgingival crevice exceeds far beyond that was characterized previously. Kroes et al. noted that less than a quarter (24%) of phylotypes identified with metagenomic techniques could be recovered by cultivation and that almost half of the subgingival phylotypes identified with a combination of molecular and culture-based techniques had not been characterized previously [14]. In fact, another study estimated that in human oral cavity approximately 68% of all bacterial taxa were still uncultivated [6].

Although molecular techniques have been used to compare subgingival plaques in healthy hosts and those with oral diseases such as periodontitis [15], few studies have investigated in depth the oral microbiota associated with gingivitis. There are several reasons. First, the depth and breadth of sampling for oral microbiota have been insufficient in general, and the optimal parameters not determined for that of gingivitis patients in particular. Second, regarding the selection of gingival sites for plaque sampling, it was still not clear whether or which of the different sites are clinically relevant (e.g., anterior teeth or posterior teeth? supragingival plaque or subgingival plaque?). Such ambiguity severely limits meaningful data analysis and comparisons across studies, and delays the translation of the findings clinically. Third, most oral microbial surveys that enumerated 16 S sequences of PCR-amplicons have ignored potential PCR artifacts [16-19]; as a result, a comprehensive and accurate organismal landscape of most oral microbiomes, particularly those related to diseases, remained largely elusive. All these factors have confounded the assessment of microbial factors associated with gingivitis.

Employing pyrosequencing of 16 S rDNA amplicons, this article elucidated the diversity and population structure of the oral microbiota, sampled respectively from five oral ecological niches from each of the three Chinese adults with gingivitis and three without the disease. Microbiota of supragingival plaque, subgingival plaque, and saliva were characterized to test whether and how the microbiomes from the various oral ecological niches distinguished healthy hosts and those with gingivitis. Our study pinpointed a number of organisms as potential biomarkers of gingivitis, and provided important insights for the sampling and analysis strategies for unraveling gingivitis-associated microbial risk factors in human populations.

Results

Study design

The six subjects were healthy, non-smoking adults in age ranging from 21 to 39 years (Table 1). Group assignment was based on the frequency of bleeding on probing (BOP). Three subjects were assigned to the healthy group (H) based on a BOP frequency ≤5 and three subjects to the unhealthy (gingivitis) group (U) based on a BOP frequency of ≥ 20. Group H consisted of three women and Group U included two men and one woman. Bleeding indices of the individual subjects were shown in Table 1.

Table 1

Metadata for the six subjects sampled in this study.

Group	Healthy (H)			Unhealthy (U)
Subject ID	1	2	3	4	5	6
Gender	F	F	F	F	M	M
Age	22	23	21	39	25	25
Smoking	N	N	N	N	N	N
Chronic disease	N	N	N	N	N	N
BOP	5	1	1	24	37	25
MGI	1.1250	1.0357	1.0179	2.1346	2.6071	1.9286

Abbreviations: BOP - Bleeding on probing; MGI - Mazza Gingival Indexes

Sequence datasets

Five samples (each from a different oral site; see Methods) from each individual were collected and analyzed. Barcoded 16 S-rDNA amplicon sequencing using 454 Titanium (average read length of 400 bp) yield a total of 494,988 raw reads, resulting in a dataset of 258,385 reads (after stringent quality assessment and control measures; Methods). The number of reads per sample ranged from 4,405 to nearly 13,562, with an average of 8,612 (Table 2).

Table 2

Estimates of species diversity for the samples.

Host ID	Site-specific sample	Sample ID	Raw reads	Reads analyzed	Unique sequence	Good's Coverage	OTUs at 3% difference	Ace	Chao 1
1	S	H1	18805	11580	3120	98.13%	687	902.15	944.54
	A-sup	H2	18448	9764	2307	98.56%	464	603.62	634.17
	A-sub	H3	15895	8035	2748	97.98%	597	740.87	775.64
	P-sup	H4	13974	7467	2142	96.69%	684	966.39	925.12
	P-sub	H5	14224	7730	2528	97.12%	702	932.10	923.01
2	S	H6	20982	11907	3264	98.35%	685	877.19	884.03
	A-sup	H7	19416	10935	2415	98.99%	379	491.50	490.02
	A-sub	H8	17295	9539	2060	98.66%	390	533.27	523.25
	P-sup	H9	22178	10085	2653	97.98%	631	846.61	842.29
	P-sub	H10	23105	10809	3160	98.06%	736	928.09	937.33
3	S	H11	19146	10970	3229	98.11%	612	847.56	878.51
	A-sup	H12	12515	5476	1933	97.11%	515	672.34	680.37
	A-sub	H13	13289	6581	1829	97.80%	466	608.60	631.71
	P-sup	H14	13105	6615	1896	97.57%	494	657.40	661.27
	P-sub	H15	12673	6456	1943	97.23%	539	728.34	757.23
4	S	U1	24258	13562	3929	98.50%	671	880.91	876.01
	A-sup	U2	18719	9591	2190	98.20%	495	700.72	713.79
	A-sub	U3	16282	7651	2636	97.56%	638	819.36	805.22
	P-sup	U4	20539	10035	3045	98.33%	617	766.07	749.34
	P-sub	U5	11515	5272	1963	96.47%	597	776.19	772.56
5	S	U6	20527	12890	3150	98.02%	722	1012.09	1001.18
	A-sup	U7	14307	7813	2436	97.82%	589	746.85	748.61
	A-sub	U8	14763	8078	2793	97.83%	621	785.67	781.26
	P-sup	U9	14787	7268	2213	97.14%	589	818.53	828.20
	P-sub	U10	16246	8916	2902	97.73%	656	858.40	934.10
6	S	U11	15739	8819	2569	97.74%	606	806.46	824.90
	A-sup	U12	12849	5407	2013	96.10%	570	819.64	810.82
	A-sub	U13	17990	8484	2844	97.15%	737	996.50	984.13
	P-sup	U14	9112	4405	1330	95.87%	475	678.74	671.08
	P-sub	U15	12305	6249	2189	96.13%	698	959.80	960.71

Richness and diversity analysis based on Operational Taxonomic Units

Clustering the unique sequences into operational taxonomic units (OTUs) at a 3% genetic distance resulted in 464~737 different "species level" phylotypes per microbiome (Table 2). For all of the oral microbial communities analyzed, the number of OTU detected was very close to the total number of OTU estimated by Chao1 and ACE richness indicators. The average level of Good's coverage was over 97% in all samples, indicating that about three new phylotypes would be expected for every 100 additional sequenced reads. This level of coverage suggested that the 16 S rDNA sequences identified represent the majority of bacterial members present in saliva and plaque samples in the current study. The individual rarefaction curves showed a similar pattern of increasing diversity that has not yet reached saturation (Figure 1). Comparisons of the rarefaction curves in the healthy (H) and gingivitis (U) populations for the sampled sites of saliva and plaque showed that the two host-groups displayed similar richness of bacterial OTUs at 97% identity level.

Figure 1

Rarefaction curves for H and U groups at the sampled sites of saliva and plaque. For both saliva plaque microbiomes, H and U Groups displayed similar phylogenetic diversity at 97% identity level (based on up to 4,000 sequences per sample).

Comparisons of community structures

To determine whether the microbiota from the saliva and plaque distinguished healthy hosts and those with gingivitis, multivariate analyzes were applied to compare the overall structure of microbiota from each oral ecological niche based on FastUnifrac-derived and thetaYC-based distance matrices. FastUniFrac [20] allows pairwise comparisons of the evolutionary distances between two microbial communities and measures similarities among microbial community-structures. In the PCoA analysis, under a weighted UniFrac scheme, segregation between H and U groups was observed (p < 0.001) when all samples or when only plaque samples were considered, but not when only saliva samples were considered (Figure 2A). Moreover, regardless of their host-group affiliation, saliva microbiota formed a distinct cluster from the plaque microbiota (p = 0.034) (Figure 2B, C). The thetaYC-based PCoA analysis showed consistent results (Figure 3A, B). When only plaque samples were considered, the structural segregation between "H" and "U" groups was more discriminating (p = 0.001) (Figure 3C) than when all samples were included (p = 0.005) (Figure 3A). Therefore, gingivitis- and healthy-gingival-microbiomes can be distinguished based on the distinct community structures of plaque microbiomes, but not the salivary microbiomes.

Figure 2

Comparisons of bacterial community structures as measured by FastUniFrac. Community structures from H and U Groups (A) or from the different sites (B) were interrogated using principal coordinate analysis (PCoA) and clustering analysis of the weighted UniFrac distance matrix. Each point corresponds to a microbial community, with color indicating its category. Percentages of variation explained by the plotted principal coordinates were indicated on the axes.

Figure 3

ThetaYC-based analysis of bacterial community structures. Community structures from H and U Groups (A) or from the different sites (B) were interrogated using principal coordinate analysis (PCoA) of thetaYC distance matrix. Each point corresponds to a microbial community, with color indicating its category. Percentages of variation explained by the plotted principal coordinates were indicated on the axes.

Taxonomy-based characterization of oral microbiota

Bacterial phyla and genera were identified and quantified through taxonomic assignment against reference databases using MOTHUR, which reveal their relative abundance in all of the plaque and saliva microbiota (Figure 4; only phyla were shown). All sequences were found distributed in 11 bacterial phyla that include six predominant phyla commonly encountered in the oral cavity: Firmicutes, Proteobacteria, Bacteroidetes, Actinobacteria, Fusobacteria and TM7 [6,21]. The relative abundance of all plaque phyla detected in each of the two host groups suggested that significant differences for most of the phyla were found between hosts with or without gingivitis, except for Firmicutes, Proteobacteria and Spirochaetes (Figure 5). Among those gingivitis-associated phyla, Actinobacteria and Bacteroidetes were gingivitis-depleted while the remaining five phyla were gingivitis-enriched (Figure 5).

Figure 4

Predominant phylotypes in each sample. Over 90% of the diversity in each sample was contributed by the six phyla. However, variations were found in their relative abundances. See Methods for abbreviations.

Figure 5

Comparisons of bacterial taxonomic profiles of H and U groups at phylum level based on oral "CORE" database. The relative abundance of each taxon between the H and U groups were compared using Metastats (*: 0.01

Comparisons of bacterial taxonomic profiles of H and U groups at phylum level based on oral "CORE" database. The relative abundance of each taxon between the H and U groups were compared using Metastats (*: 0.01