ENVIRONMENTAL MICROBIOLOGY

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Signature of Microbial Dysbiosis in

Periodontitis
Vincent Meuric,a,b Sandrine Le Gall-David,b Emile Boyer,a,b Luis Acuña-Amador,c
Bénédicte Martin,b Shao Bing Fong,b Frederique Barloy-Hubler,c
Martine Bonnaure-Malleta,b

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CHU Rennes, Pôle Odontologie, Rennes, Francea; Université de Rennes 1, EA 1254, INSERM 1241, Equipe de
Microbiologie, Rennes, Franceb; CNRS, UMR 6290, IGDR, Rennes, Francec

ABSTRACT Periodontitis is driven by disproportionate host inflammatory immune re-

sponses induced by an imbalance in the composition of oral bacteria; this instigates mi- Received 23 February 2017 Accepted 2 May
2017
crobial dysbiosis, along with failed resolution of the chronic destructive inflamma-
Accepted manuscript posted online 5 May
tion. The objectives of this study were to identify microbial signatures for health and 2017
chronic periodontitis at the genus level and to propose a model of dysbiosis, includ- Citation Meuric V, Le Gall-David S, Boyer E,
ing the calculation of bacterial ratios. Published sequencing data obtained from sev- Acuña-Amador L, Martin B, Fong SB, Barloy-
Hubler F, Bonnaure-Mallet M. 2017. Signature
eral different studies (196 subgingival samples from patients with chronic periodon- of microbial dysbiosis in periodontitis. Appl
titis and 422 subgingival samples from healthy subjects) were pooled and subjected Environ Microbiol 83:e00462-17. https://doi
to a new microbiota analysis using the same Visualization and Analysis of Microbial .org/10.1128/AEM.00462-17.

Population Structures (VAMPS) pipeline, to identify microbiota specific to health and Editor Andrew J. McBain, University of
Manchester
disease. Microbiota were visualized using CoNet and Cytoscape. Dysbiosis ratios, de- Copyright © 2017 American Society for
fined as the percentage of genera associated with disease relative to the percentage Microbiology. All Rights Reserved.
of genera associated with health, were calculated to distinguish disease from health. Address correspondence to Vincent Meuric,
Correlations between the proposed dysbiosis ratio and the periodontal pocket depth vincent.meuric@univ-rennes1.fr.
F.B.-H. and M.B.-M. contributed equally to this
were tested with a different set of data obtained from a recent study, to confirm the
work.
relevance of the ratio as a potential indicator of dysbiosis. Beta diversity showed sig-
nificant clustering of periodontitis-associated microbiota, at the genus level, accord-
ing to the clinical status and independent of the methods used. Specific genera
(Veillonella, Neisseria, Rothia, Corynebacterium, and Actinomyces) were highly preva-
lent (⬎95%) in health, while other genera (Eubacterium, Campylobacter, Treponema,
and Tannerella) were associated with chronic periodontitis. The calculation of dysbio-
sis ratios based on the relative abundance of the genera found in health versus peri-
odontitis was tested. Nonperiodontitis samples were significantly identifiable by low
ratios, compared to chronic periodontitis samples. When applied to a subgingival
sample set with well-defined clinical data, the method showed a strong correlation
between the dysbiosis ratio, as well as a simplified ratio (Porphyromonas, Treponema,
and Tannerella to Rothia and Corynebacterium), and pocket depth. Microbial analysis
of chronic periodontitis can be correlated with the pocket depth through specific
signatures for microbial dysbiosis.
IMPORTANCE Defining microbiota typical of oral health or chronic periodontitis is diffi-
cult. The evaluation of periodontal disease is currently based on probing of the peri-
odontal pocket. However, the status of pockets “on the mend” or sulci at risk of
periodontitis cannot be addressed solely through pocket depth measurements or
current microbiological tests available for practitioners. Thus, a more specific micro-
biological measure of dysbiosis could help in future diagnoses of periodontitis. In
this work, data from different studies were pooled, to improve the accuracy of the
results. However, analysis of multiple species from different studies intensified the
bacterial network and complicated the search for reproducible microbial signatures.
Despite the use of different methods in each study, investigation of the microbiota
at the genus level showed that some genera were prevalent (up to 95% of the sam-

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ples) in health or disease, allowing the calculation of bacterial ratios (i.e., dysbiosis
ratios). The correlation between the proposed ratios and the periodontal pocket
depth was tested, which confirmed the link between dysbiosis ratios and the sever-
ity of the disease. The results of this work are promising, but longitudinal studies
will be required to improve the ratios and to define the microbial signatures of the
disease, which will allow monitoring of periodontal pocket recovery and, conceiv-
ably, determination of the potential risk of periodontitis among healthy patients.
KEYWORDS chronic periodontitis, health, microbiota, dysbiosis ratio

C hronic periodontitis (CP) is a type of chronic inflammation characterized by alveolar

bone loss, with intermittent periods of remission and relapse. CP is currently
considered an infection, mainly due to increases in bacteria in the sulcus, leading to the

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formation of a periodontal pocket (for review, see references 1 and 2). The major
pathogen linked to CP is Porphyromonas gingivalis, with bacterial partners such as
Treponema denticola and Tannerella forsythia. These three bacteria have been consid-
ered the major pathogenic “red complex” since 1998 (3). However, recent advances
from metagenomic studies have developed a new model of periodontal disease
pathogenesis. CP does not result from individual pathogens but rather from polymi-
crobial synergy and dysbiosis (4) associated with a dysregulated immune response
inducing inflammation-mediated tissue damage (5). Host genetic components have
also been implicated in CP, with multiple genes contributing cumulatively to the host’s
overall disease risk (or protection) through effects on the host immune response and
the microbiome (6). Since the Human Microbiome Project (HMP) (7), microbiota have
been analyzed based on partial sequencing of the 16S rRNA gene, with different numbers
of healthy and CP samples. However, comparisons between studies are difficult because of
the differences in the methods used (i.e., clinical examination and diagnosis of periodontitis
and oral health, sample collection protocols, DNA extraction protocols, and analysis of
hypervariable regions of the 16S rRNA gene). Because there is growing interest in the
human microbiome, despite the difficulties mentioned earlier, the use of independent
studies to look for “signal in the noise” should proceed as suggested previously (8), through
reanalysis of all data with the same protocol. The difference between periodontal health-
associated and disease-associated microbiota should be larger than the technical variations
of the different studies, which would enable the identification of microbial signatures using
next-generation sequencing (NGS) technologies. The first objective of this study was to
explore the disease-associated changes in the subgingival microbiota at the genus level,
using a unique Visualization and Analysis of Microbial Population Structure (VAMPS)
pipeline (9), for beta diversity (Bray-Curtis dissimilarity) with a large number of samples
(from 6 different studies) and to confirm that the microbiota identified did not cluster
according to the methods used (primer or study type). Subgingival microbiota from
patients with diagnosed chronic periodontitis (196 samples) and from healthy subjects (422
samples), as well as external control samples (from dentine caries, supragingival plaque,
and the midvagina), were included. The second objective was to determine a dysbiosis ratio
of bacteria that could predict health or disease severity from the subgingival samples and
finally to test the ratio with an independent cohort of patients with well-described peri-
odontal pocket measurements.

RESULTS
Microbial community structure analysis. Using a matrix correlation analysis, the
possible clustering of microbiota according to the nature of the primers used, the site
of sampling, or the study investigated was explored. Despite various studies, the
analyzed data clustered into five groups according to the clinical status (healthy or CP)
or the sampling site, as shown by the three-dimensional (3D) principal-coordinate
analysis (PCoA) plots (Fig. 1). Healthy subgingival samples were primarily spread into
two main clusters; control samples were clearly separated into two other clusters,
corresponding to saliva and dentine caries/vagina, while the majority of CP samples

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FIG 1 Different views of 3D PCoA plots illustrating the beta diversity of bacterial populations as a function of sampling site and diagnosis. Light blue,
supragingival samples; dark blue, healthy subgingival samples; green, outgroups (saliva, midvagina, and dentine caries samples); red, CP samples.
Percentages represent percent explained variance.

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FIG 2 Unrooted tree displaying genus Bray-Curtis beta-diversity clustering of microbiota and pie charts
related to the sample origins within each cluster. The tree was prepared using Figtree 1.4.2 software. The
distribution of microbiota in each cluster is represented by pie charts, with different colors representing
different sampling sites (supragingival [Sup] in light blue, saliva in light green, dentine caries in medium
green, and midvagina in dark green) and diagnosis for subgingival (Sub) samples (healthy in dark blue,
shallow in yellow, and CP in red). Percentages correspond to the number of samples from a specific
sampling site in a given cluster relative to the total number of samples from the same sampling site.

were found in a fifth cluster. Two-dimensional (2D) beta-diversity analysis showed the
precise distribution of the samples in the five clusters (Fig. 2). The search for an
association between clusters and primers and/or study type showed that the fourth
cluster was associated with V3V4 16S rRNA primers (correlation r ⫽ 0.537; P ⬍ 0.001)
and with the study by Kianoush et al. (10), which used those specific primers (correla-
tion r ⫽ 0.608; P ⬍ 0.001). No other correlations with primers were found. The two
healthy clusters (clusters 1 and 2) were characterized by subgingival and supragingival
samples in similar proportions (Fig. 2, light blue and dark blue sections). Focusing on
healthy subgingival samples, the main difference between the two healthy clusters
(clusters 1 and 2) was in the distribution of samples from the HMP study and from the
other studies in the clusters; 225/323 samples from the HMP study were clustered in
healthy cluster 2, while healthy cluster 1 was richer in samples derived from the other
studies (44/99 samples). Cluster 3 was characterized by saliva, as 91% of the saliva
samples (258/284 samples) were grouped in this cluster (correlation r ⫽ 0.892; P ⬍
0.001) (Fig. 2). Cluster 4 was characterized by dentine caries samples (73% [80/110
samples]; correlation r ⫽ 0.603; P ⬍ 0.001) and midvagina samples (100% [60/60
samples]; correlation r ⫽ 0.638; P ⬍ 0.001). Finally, cluster 5 contained 90% of the
CP samples (176/196 samples; correlation r ⫽ 0.708; P ⬍ 0.001).
It is interesting to note that 10% of the CP samples were found in the two healthy
clusters (19/196 samples) and contained similar microbiota (analyzed on the basis of
beta diversity), at the genus level, as dentine caries samples and/or midvagina samples
(1/196 samples). Conversely, 16% of the healthy subgingival samples (69/422 samples)
and 17% of the dentine caries samples (19/110 samples) were found in cluster 5.
Microbiota richness and alpha diversity in subgingival samples. A cluster com-
parison showed that the sampling depth (number of reads sequenced) was greater in
healthy subgingival clusters 1 and 2 than in cluster 5. Nevertheless, no significant
difference between healthy subgingival samples and CP samples of cluster 5 was found
(Fig. 3). The observed richness (S) was lower in the CP samples of cluster 5 than in the

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FIG 3 Alpha diversity index values. Comparisons of microbiota sampling depth, observed richness
(number of different taxa per sample), and diversity (Shannon-Weaver index) in subgingival samples of
healthy clusters 1 and 2 (dark blue) and either CP samples (red) or healthy subgingival samples (light
blue) of cluster 5 were performed. *, P ⬍ 0.05; **, P ⬍ 0.01.

samples of healthy clusters 1 and 2 and the healthy subgingival samples of cluster 5
(Fig. 3). However, the Shannon-Weaver diversity index values showed that the diversity
of healthy cluster 2 was significantly greater than the diversity of healthy cluster 1 and
that of all samples from cluster 5, which were similar.
Patterns of microbial communities in subgingival samples (genus level). Gen-
era that were present in at least 95% of all healthy subgingival samples or 95% of
the CP samples from cluster 5 are presented in Fig. 4A and B, respectively. Results
showed that healthy subgingival samples were dominated by 8 major genera, i.e.,
Fusobacterium, Actinomyces, Streptococcus, Neisseria, Capnocytophaga, Prevotella,
Corynebacterium, and Rothia, and 6 minor genera, i.e., Leptotrichia, Veillonella,
Porphyromonas, Granulicatella, Kingella, and Gemella. Associations were found be-
tween Fusobacterium and Prevotella, Actinomyces, and Rothia and between Lepto-
trichia and Porphyromonas. Common genera found in CP samples were less abun-
dant, with 4 major genera, Treponema, Porphyromonas, Prevotella, and Fusobacterium,
followed by Streptococcus, Eubacterium, Tannerella, and Campylobacter genera. Only one
association was found, between Eubacterium and Treponema, while Fusobacterium and
Treponema presented a negative correlation.
Calculation of dysbiosis ratios of bacteria. The dysbiosis ratios of the genera found
mainly in CP samples (Eubacteria, Campylobacter, Treponema, and Tannerella) to the
genera found mainly in healthy samples (Veillonella, Neisseria, Rothia, Corynebacterium,
and Actinomyces) were significantly different among the samples according to their
diagnosis. The dysbiosis ratios for healthy subgingival samples (from the HMP, n ⫽ 323,

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FIG 4 Patterns of subgingival microbial communities. (A) Patterns of genera present in at least 95% of all healthy
subgingival samples. (B) Patterns of genera present in at least 95% of all CP samples from cluster 5. Edges represent
1 (thin line) or 2 or 3 (thick line) significant correlations between genera (green, positive; red, negative). Node colors
represent the numbers of partners, ranging from 1 (green) to 7 (dark orange). Node sizes represent the abundance
of each taxon.

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FIG 5 Subgingival dysbiosis ratios of Eubacterium, Campylobacter, Treponema, and Tannerella to Veillo-
nella, Neisseria, Rothia, Corynebacterium, and Actinomyces. (A) Comparisons between healthy, shallow,
and CP samples from all clusters. (B) Comparisons between clusters 1 and 2 and cluster 5 for healthy,
shallow, and CP samples.

ratio of 0.016; from the other studies, n ⫽ 99, ratio of 0.021) yielded a median ratio of
0.018; the samples from shallow sites had a ratio of 0.071, and the CP samples had a
ratio of 1.229 (P ⬍ 0.001) (Fig. 5A).
Although different clustering was achieved through beta-diversity analysis, no
significant difference in the ratios of clusters 1 and 2 according to the clinical status
(healthy, shallow, or CP) was found. Pooling of samples according to clinical status was
performed, and the resulting ratios were compared to the ratios for cluster 5, as shown
in Fig. 5B.
The dysbiosis ratio found for CP samples from cluster 5 (ratio of 1.510) was
significantly greater than the ratios for the majority of samples from clusters 1 and 2
(healthy subgingival samples, ratio of 0.015; shallow samples, ratio of 0.052; CP samples,
ratio of 0.088) and was also significantly greater than the ratio for healthy subgingival
samples (ratio of 0.184) from cluster 5 (P ⬍ 0.001). In clusters 1 and 2, the dysbiosis
ratios for CP samples were similar to the ratios for shallow sites. These two groups were
significantly different from the healthy subgingival samples (P ⬍ 0.05) in the same
cluster.
Healthy subgingival samples (n ⫽ 69) belonging to cluster 5 exhibited a dysbiosis
ratio (ratio of 0.184) significantly different from that for the other healthy subgingival
samples (ratio of 0.015) and also from that for the majority of the CP samples (cluster
5) (ratio of 1.510). These results confirmed the possible difference of these healthy
subgingival microbiota (P ⬍ 0.001) from those of healthy clusters 1 and 2. Their ratio
was not significantly different from the CP sample ratios in clusters 1 and 2, which could
be considered “on the mend.”
Validation of the dysbiosis ratio. A different data set, from Bizzarro et al. (11) and
containing well-described samples (pocket depths of 2 to 8 mm), was used as an
external control to confirm the relevance of the bacterial dysbiosis ratio. The dysbiosis

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FIG 6 Correlation between pocket depth (PD) and dysbiosis. Samples from Bizzarro et al. (11) were analyzed by
VAMPS, followed by calculation of the dysbiosis ratios. (A) Ratios of Eubacterium, Campylobacter, Treponema, and
Tannerella to Veillonella, Neisseria, Rothia, Corynebacterium, and Actinomyces. (B) Simplified ratios of Porphyromonas,
Treponema, and Tannerella to Rothia and Corynebacterium.

ratio at the genus level was correlated with the periodontal pocket depth (r ⫽ 0.655;
P ⬍ 0.001) (Fig. 6A). These results, based on data for 37 patients (147 samples collected
at different times, with different procedures for periodontal treatment), confirmed the
link between dysbiosis and the depth of the periodontal pocket. The simplified ratio of
Porphyromonas, Treponema, and Tannerella to Rothia and Corynebacterium showed a
similar correlation (r ⫽ 0.659; P ⬍ 0.001) (Fig. 6B).

DISCUSSION
Many studies have been published since the Human Microbiome Project in 2009,
increasing the volume of microbiota data available for the research community. How-
ever, comparisons between studies are challenging, at least at the species level,
because of the use of different methods. This issue is a real limitation to understanding
disease, as is the small number of samples in each study. Additionally, findings are more
complicated for healthy subgingival samples, which usually represent less than one-half
of the samples included in the studies (12, 13). This work is a taxon-based analysis, at
the genus level, of sequence reads from several studies. Studying a large number of
samples minimized individual variations and overcame technical variations by increas-
ing the effective sample size. Such an analysis had already been proposed in a recent
study of the microbiota in obesity (8). Studies with described healthy samples (sulci of
ⱕ3 mm) and CP samples (pocket depths of ⱖ5 mm) and available raw sequence data
in data banks were chosen. Data from the HMP resources (two different pairs of primers
used) were added to increase the number of healthy subgingival samples with available
microbiota data from 99 samples to 422 samples. The different microbiota clustered
either by sampling site, such as the outgroups used as controls for this study (saliva
samples in cluster 3 and dentine caries and vagina samples, which are both rich in
Lactobacillus, in cluster 4), or by clinical status, such as subgingival samples (healthy
samples in clusters 1 and 2 and CP samples in cluster 5). CP sites either can show
greater microbial diversity and observed richness, compared with healthy subgingival
sites (14, 15), or can present no significant difference in microbial diversity, as reported
for health versus periodontitis (16). Thus, the large number of samples surpasses the
technical variations, at least at the genus level, with the primers used in the different
studies, and the difference between periodontal health and disease is larger than the
technical variations, as described by Kirst et al. (16). No difference between the healthy
subgingival and supragingival samples was found when beta-diversity analysis was
performed at the genus level, as described previously (17). Ninety percent of the CP
samples were found in cluster 5. To define cluster 5 as a “periodontitis cluster” by beta
diversity was appealing. However, cluster 5 also contained healthy subgingival samples,
indicating that further investigations are necessary to understand and to develop
prediction markers for chronic periodontitis.

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A core community (genera present in at least 50% of the samples) is usually

identified in publications and provides a basis for disease diagnosis, prevention, and
therapeutic targets (18, 19). However, the variability of genera expands as the sample
size increases, thus limiting its use for establishing an easy microbiological marker for
dysbiosis. In this work, genera present at higher prevalence in at least 95% of the
samples were used to determine the genera implicated in health or in favor of the
disease. The genera used to calculate the dysbiosis ratio in favor of periodontitis were
Treponema, Campylobacter, Eubacterium, and Tannerella. These genera were identified
at high levels and high prevalence in CP samples, compared to healthy samples. The
genera include well-identified species (Tannerella forsythia, Treponema denticola, C.
rectus, and E. nodatum) that are strongly associated with disease (3, 20–22). It should be
noted that some species, such as the newly cultivated Tannerella clone BU063 (23, 24),
which is thought to be health associated, are also found in active periodontal sites (25);

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therefore, this species is still controversial. Despite a significant difference in the
abundance of the Porphyromonas genus (including P. gingivalis, which is strongly
associated with periodontitis) between healthy samples (3.35%) and CP samples (13%),
the genus was excluded in the first dysbiosis ratio because of its similar prevalence
rates. Because the lowest abundance value among genera accounting for the CP
calculations was that for Campylobacter (1.9%), this value was chosen as a cutoff value
to minimize the number of genera used for the health calculations, i.e., Rothia,
Corynebacterium, Actinomyces, Veillonella, and Neisseria. Capnocytophaga and Leptotri-
chia were not included because of their high rates of prevalence in CP samples (more
than 90%; data not shown). Species belonging to the genus Rothia have been repeat-
edly described as being members of oral communities associated with periodontal
health (26–31) or at least as being more predominant in health (28). In the same way,
Corynebacterium appeared to be more associated with healthy subgingival biofilm (32,
33). Moreover, Rothia and Corynebacterium were among the bacteria that showed the
greatest increases after periodontal treatment (34), while a study suggested that
Corynebacterium might be considered a putative periodontal protector (35). Veillonella
and Actinomyces have been negatively correlated with clinical markers in CP (36), and
Neisseria was found in inactive sites (25). The calculated dysbiosis ratios distinguish
clearly healthy subgingival samples from CP samples.
Shallow samples were divided into two groups, which can be easily explained based
on the origin of the samples (healthy subgingival sites in mouths presenting chronic
periodontitis). Two-thirds of the samples had low ratios (clusters 1 and 2) and could be
considered microbiologically healthy. The remaining one-third of the samples (cluster
5) presented high ratios, certainly due to contamination of the sampling sites by
bacteria from surrounding CP sites, and could be considered at risk of periodontitis.
Thus, shallow samples may represent an intermediate stage in disease development, as
proposed by Griffen et al. (14).
Healthy subgingival samples were divided into three groups. Two of the groups
(clusters 1 and 2) presented the same low ratios and described an absence of dysbiosis.
The third group had a higher dysbiosis ratio, similar to those for shallow sites and CP
samples from clusters 1 and 2 but significantly lower than those for CP or shallow
samples from cluster 5. Because healthy patients from the HMP were defined as
patients with pockets depths of ⬍4 mm, some of them could have explained this
high-ratio group; however, healthy patients from other studies (19/99 subjects) were
also included in this group. This result is similar to those of Zhou et al., in which a few
healthy subjects with indicators of disease, such as increases in Treponema, were
detected (37). Therefore, patients who presented with relatively high ratios could be
considered at risk of periodontitis.
Conversely, a few CP samples with deep periodontal pockets (i.e., ⱖ5 mm) had low
dysbiosis ratios. An hypothesis of appropriate host responses (such as a stronger
immune response and/or better hygiene) could explain this discrepancy between the
dysbiosis ratios and the diagnoses; these patients might be microbiologically on the
mend, as revealed by both the clustering and the dysbiosis ratios. Another hypothesis

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is a sampling issue between the top and base of the periodontal pocket (to be
discussed later). To study the hypothesis of microbiota on the mend and the calculated
dysbiosis ratios, a recent study presenting follow-up findings after treatment, with
well-defined depths of periodontal pockets, was performed (11). The study was con-
ducted with a different set of primers (for V5V7) and allowed testing of the dysbiosis
ratios at the genus level with a new set of primers that had not been used to determine
the ratios. Consequently, this comparative analysis can be considered a validation
experiment for the ratios. A strong correlation between the dysbiosis ratios and the
pocket depths was observed, thus highlighting the value of calculating the dysbiosis
ratio (using the selected genera of our study) as a microbial signature to evaluate the
microbiota of chronic periodontitis.
A major concern at the beginning of this work was the capacity to identify species
with multiple data sets. However, the V1V2 and V5V7 primers used in three studies

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were not suitable for species identification. At the genus level, as reported by Bizzarro
et al. (11), the proposed dysbiosis ratio (even as a simplified dysbiosis ratio) is a good
microbial signature calculated using the online VAMPS software. Indeed, Rothia and
Corynebacterium were the major healthy genera found and, although Porphyromonas
was found in both health and disease, its abundance increased significantly in disease
(from 3.34% to 13%). The result was interesting because it was found to be similar to
the precedent ratio (correlation with pocket depth, r ⫽ 0.659; P ⬍ 0.001). However, the
simplified ratio needed more adjustment, because 43 of 196 CP samples presented
neither of the two healthy genera and a value of 0.1% was attributed for the calculation
(see Materials and Methods [“Calculation of dysbiosis ratios of bacteria” section]).
Finally, using ratios, some data points still showed discrepancies in predicting the
periodontal status. The variability in microbial composition and spatial distribution
could explain these results. Deep periodontal pockets in CP patients may present
gradients of oxygen tension, pH, and nutrients, as well as host defense factors, from the
base of the pocket to the top (opening). This may explain why some genera (Porphy-
romonas and Treponema) are typically found at the base of the pocket (38, 39).
However, the sampling could induce bias even after careful removal of the supragin-
gival plaque. Healthy genera may be found predominantly at the top (opening) of the
pocket, with the genera more closely associated with CP being located at the base of
the pocket. Indeed, while the architecture of the periodontal pocket has not yet been
clearly studied with the use of NGS analysis, the importance of the biogeography of the
microbiome on the micron scale was clearly shown recently (40).
In conclusion, this study aimed to define ratios of bacteria as microbial signatures,
after the analysis of publicly available raw data from different studies, independent of
the technical methods used to generate the data. These ratios allowed the differenti-
ation of healthy and diseased microbiota in the majority of samples. Standardized
protocols for sampling and complete metadata in public data banks are necessary to
study dysbiosis in oral health and to improve the proposed dysbiosis ratios. The
addition of specific perioprotectors and potential specific pathogens to the dysbiosis
calculations could also be promising. Longitudinal studies are necessary to predict
exact pockets that are microbiologically on the mend or sulci with a risk of periodon-
titis.

MATERIALS AND METHODS

Microbiome data sets for comparison. Read sequences from healthy and CP subgingival samples
from five different studies, i.e., those by Abusleme et al. (12), Kirst et al. (16), Griffen et al. (14) (shallow
site samples also included), Zhou et al. (41), and Camelo-Castillo et al. (13), were retrieved from either the
NCBI Sequence Read Archive (SRA) or the metagenomics (MG)-RAST server (Table 1). Twenty-four
samples from patients with chronic periodontitis who were recruited between June 2010 and September
2011 at the University Hospital (Rennes, France), which were analyzed using V3V4 primers, were added
(E. Boyer, S. Le Gall-David, Y. Deugnier, M. Bonnaure-Mallet, and V. Meuric, unpublished data). Each data
set was manually imported into VAMPS (https://vamps.mbl.edu), while numerous healthy subgingival
samples were added from the HMP (two different subgingival data sets, using V1V3 and V3V5 primers,
available in VAMPS [9]). Three mouth control microbiota data sets from the HMP (saliva and supragin-
gival, both V1V3 and V3V5 regions, available in VAMPS) and dentine caries data from the study by

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TABLE 1 Subgingival microbiota samples used in this study

No. of subgingival microbiota samples
CP
Follow up after 16S rRNA
Authors Accession no. Health Shallowa Diagnosis treatment gene regions
Abusleme et al. (12) GenBank SRA SRA051864 10 44 V1V2
Kirst et al. (16) GenBank BioProject PRJNA269205 25 25 V1V3
Griffen et al. (14) GenBank SRA SRP009299 29 29 29 V1V2 and V4
Camelo-Castillo et al. (13) MG-RAST 12161 22 60 V1V3
Zhou et al. (37) GenBank SRA SRA062091 13 18 V1V3
Boyer et al. (unpublished) In progressb 24 V3V4
HMP (7) 119 V1V3
HMP (7) 204 V3V5
Bizzarro et al. (11) GenBank BioProject PRJNA289294 37c 110c V5V7

Downloaded from http://aem.asm.org/ on July 18, 2020 by guest

aSitesdefined as healthy in patients with periodontitis (14).
bData are available on VAMPS (data set designated Y_Hemoparo).
cData on the CP microbiota from patients with follow-up findings after treatment were used to confirmed the dysbiosis ratio hypothesis (11).

Kianoush et al. (10) (V3V4 regions; BioProject accession no. PRJEB5178) were used. One midvagina
microbiome data set from the HMP (V1V3 region, available in VAMPS) was used as an external mouth
control. Finally, the data set from the study by Bizzarro et al. (11), containing well-described sample
pocket depths (from 2 to 8 mm), was used to independently challenge the relevance of the dysbiosis
ratio of bacteria involved in periodontitis.
Ecology diversity and taxonomic identifications. Reads from the different data sets were analyzed
with VAMPS, using default parameters for taxonomic assignment to the genus level through the Global
Alignment for Sequence Taxonomy (GAST) process and using the Ribosomal Database Project (RDP)
classification to produce the best taxonomic assignment for each read. Reads identified as Archaea,
Eukarya, or organelle and unknown reads were excluded from further analysis. The frequency of each
taxonomic assignment in the data set was reported as a percentage (number of reads with the
taxonomic assignment relative to the total number of reads in the data set). Alpha diversity, as the
observed richness, and Shannon-Weaver index values were determined from the raw data sets. Differ-
ences between microbiota structures (beta diversity) were assessed using a 2D PCoA tree based on the
Bray-Curtis distances, through VAMPS. Samples were divided into five clusters (clusters 1 to 5); visual-
izations were performed using Figtree software (version 1.4.2), and 3D PCoA plots were generated using
Emperor software. Relative abundances were studied when the average abundance was above 1% in at
least one sample. Assessments of significant patterns of microbial cooccurrence or mutual exclusion at
the genus level were performed using Cytoscape 3.2.1 (42) and the CoNet plugin (43). Only genera found
in the great majority (at least 95%) of the healthy subgingival samples or the CP samples (from cluster
5) are represented.
Calculation of dysbiosis ratios of bacteria. To measure the dysbiosis, a first ratio, based on the
relative abundance of genera highly prevalent (⬎95%) in CP samples (Eubacterium, Campylobacter,
Treponema, and Tannerella) versus genera highly prevalent (⬎95%) in healthy microbiota (Veillonella,
Neisseria, Rothia, Corynebacterium, and Actinomyces), was calculated. The ratios were normalized between
samples using GraphPad Prism 6 software before comparisons. A second simplified ratio of Porphyromo-
nas, Treponema, and Tannerella versus Rothia and Corynebacterium was also tested. When no specific
genus was detected, a value of 0.1% was attributed (because “not detected” does not mean “absence”).
Statistical analysis. Normality tests for data distribution were performed. Data were studied with the
Spearman correlation test for correlations of biological origins, primers used, published sample origins,
and microbiota clusters. Observed richness (number of taxa per sample), Shannon-Weaver index values,
and dysbiosis ratios of the genera found in disease to the genera found in health were analyzed with a
Kruskal-Wallis test (nonparametric analysis of variance). Tests were carried out using GraphPad Prism 6
software and were considered significant with P values of ⬍0.05. The significant patterns of microbial
cooccurrence and mutual exclusion were analyzed as described by Faust et al. (43); a compilation of
statistical analyses (Spearman and Pearson correlations and Bray-Curtis and Kullback-Leibler dissimilarity
measures) was used, with a threshold set at 0.5. The data matrix was randomized by 100 row-wise
permutations. The P values were adjusted with the Benjamini-Hochberg false discovery rate (FDR)
correction for the number of tests, retaining only P values of ⬍0.05. Finally, the ratios of genera and
pocket depths were controlled for normality, followed by the Spearman correlation test.

ACKNOWLEDGMENTS
V.M., F.B.-H., and M.B.-M. conceived and designed the research, V.M. performed the
sampling, S.L.G.-D. performed the molecular biological analyses, V.M., S.L.G.-D., E.B.,
L.A.-A., B.M., S.B.F., and M.B.-M. performed the bioinformatic and statistical analyses and
wrote the manuscript, and M.B.-M. supervised the project.
We declare no competing financial interests.

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Meuric et al. Applied and Environmental Microbiology

REFERENCES
1. Teles R, Teles F, Frias-Lopez J, Paster B, Haffajee A. 2013. Lessons learned intestinal symptoms. PLoS One 6:e23035. https://doi.org/10.1371/
and unlearned in periodontal microbiology. Periodontol 2000 62: journal.pone.0023035.
95–162. https://doi.org/10.1111/prd.12010. 20. Laine ML, Moustakis V, Koumakis L, Potamias G, Loos BG. 2013. Modeling
2. Kilian M, Chapple IL, Hannig M, Marsh PD, Meuric V, Pedersen AM, susceptibility to periodontitis. J Dent Res 92:45–50. https://doi.org/10
Tonetti MS, Wade WG, Zaura E. 2016. The oral microbiome: an update for .1177/0022034512465435.
oral healthcare professionals. Br Dent J 221:657– 666. https://doi.org/10 21. Byrne SJ, Dashper SG, Darby IB, Adams GG, Hoffmann B, Reynolds EC.
.1038/sj.bdj.2016.865. 2009. Progression of chronic periodontitis can be predicted by the levels
3. Socransky SS, Haffajee AD, Cugini MA, Smith C, Kent RL, Jr. 1998. Microbial of Porphyromonas gingivalis and Treponema denticola in subgingival
complexes in subgingival plaque. J Clin Periodontol 25:134–144. https://doi plaque. Oral Microbiol Immunol 24:469 – 477. https://doi.org/10.1111/j
.org/10.1111/j.1600-051X.1998.tb02419.x. .1399-302X.2009.00544.x.
4. Hajishengallis G. 2015. Periodontitis: from microbial immune subversion 22. Haffajee AD, Teles RP, Socransky SS. 2006. Association of Eubacterium
to systemic inflammation. Nat Rev Immunol 15:30 – 44. https://doi.org/ nodatum and Treponema denticola with human periodontitis lesions.
10.1038/nri3785. Oral Microbiol Immunol 21:269 –282. https://doi.org/10.1111/j.1399
5. Hajishengallis G, Darveau RP, Curtis MA. 2012. The keystone-pathogen -302X.2006.00287.x.

Downloaded from http://aem.asm.org/ on July 18, 2020 by guest

hypothesis. Nat Rev Microbiol 10:717–725. https://doi.org/10.1038/ 23. Leys EJ, Lyons SR, Moeschberger ML, Rumpf RW, Griffen AL. 2002.
nrmicro2873. Association of Bacteroides forsythus and a novel Bacteroides phylotype
6. Hajishengallis G, Moutsopoulos NM, Hajishengallis E, Chavakis T. 2016. with periodontitis. J Clin Microbiol 40:821– 825. https://doi.org/10.1128/
Immune and regulatory functions of neutrophils in inflammatory bone loss. JCM.40.3.821-825.2002.
Semin Immunol 28:146–158. https://doi.org/10.1016/j.smim.2016.02.002. 24. Vartoukian SR, Moazzez RV, Paster BJ, Dewhirst FE, Wade WG. 2016. First
7. Peterson J, Garges S, Giovanni M, McInnes P, Wang L, Schloss JA, Bonazzi cultivation of health-associated Tannerella sp. HOT-286 (BU063). J Dent
V, McEwen JE, Wetterstrand KA, Deal C, Baker CC, Di Francesco V, Res 95:1308 –1313. https://doi.org/10.1177/0022034516651078.
Howcroft TK, Karp RW, Lunsford RD, Wellington CR, Belachew T, Wright 25. Yost S, Duran-Pinedo AE, Teles R, Krishnan K, Frias-Lopez J. 2015. Func-
M, Giblin C, David H, Mills M, Salomon R, Mullins C, Akolkar B, Begg L, tional signatures of oral dysbiosis during periodontitis progression re-
Davis C, Grandison L, Humble M, Khalsa J, Little AR, Peavy H, Pontzer C, vealed by microbial metatranscriptome analysis. Genome Med 7:27.
Portnoy M, Sayre MH, Starke-Reed P, Zakhari S, Read J, Watson B, Guyer https://doi.org/10.1186/s13073-015-0153-3.
M. 2009. The NIH Human Microbiome Project. Genome Res 19: 26. Moore LV, Moore WE, Cato EP, Smibert RM, Burmeister JA, Best AM,
2317–2323. https://doi.org/10.1101/gr.096651.109. Ranney RR. 1987. Bacteriology of human gingivitis. J Dent Res 66:
8. Sze MA, Schloss PD. 2016. Looking for a signal in the noise: revisiting 989 –995. https://doi.org/10.1177/00220345870660052401.
obesity and the microbiome. mBio 7:e01018-16. https://doi.org/10.1128/ 27. Aas JA, Paster BJ, Stokes LN, Olsen I, Dewhirst FE. 2005. Defining the
mBio.01018-16. normal bacterial flora of the oral cavity. J Clin Microbiol 43:5721–5732.
9. Huse SM, Mark Welch DB, Voorhis A, Shipunova A, Morrison HG, Eren https://doi.org/10.1128/JCM.43.11.5721-5732.2005.
AM, Sogin ML. 2014. VAMPS: a website for visualization and analysis of 28. Colombo AP, Boches SK, Cotton SL, Goodson JM, Kent R, Haffajee AD,
microbial population structures. BMC Bioinformatics 15:41. https://doi Socransky SS, Hasturk H, Van Dyke TE, Dewhirst F, Paster BJ. 2009.
.org/10.1186/1471-2105-15-41. Comparisons of subgingival microbial profiles of refractory periodontitis,
10. Kianoush N, Adler CJ, Nguyen KA, Browne GV, Simonian M, Hunter N. severe periodontitis, and periodontal health using the human oral mi-
2014. Bacterial profile of dentine caries and the impact of pH on crobe identification microarray. J Periodontol 80:1421–1432. https://doi
bacterial population diversity. PLoS One 9:e92940. https://doi.org/10 .org/10.1902/jop.2009.090185.
.1371/journal.pone.0092940. 29. Bik EM, Long CD, Armitage GC, Loomer P, Emerson J, Mongodin EF,
11. Bizzarro S, Laine ML, Buijs MJ, Brandt BW, Crielaard W, Loos BG, Zaura E. Nelson KE, Gill SR, Fraser-Liggett CM, Relman DA. 2010. Bacterial diver-
2016. Microbial profiles at baseline and not the use of antibiotics deter- sity in the oral cavity of 10 healthy individuals. ISME J 4:962–974. https://
mine the clinical outcome of the treatment of chronic periodontitis. Sci doi.org/10.1038/ismej.2010.30.
Rep 6:20205. https://doi.org/10.1038/srep20205. 30. Heuer W, Stiesch M, Abraham WR. 2011. Microbial diversity of supra- and
12. Abusleme L, Dupuy AK, Dutzan N, Silva N, Burleson JA, Strausbaugh LD, subgingival biofilms on freshly colonized titanium implant abutments in
Gamonal J, Diaz PI. 2013. The subgingival microbiome in health and the human mouth. Eur J Clin Microbiol Infect Dis 30:193–200. https://
periodontitis and its relationship with community biomass and inflam- doi.org/10.1007/s10096-010-1068-y.
mation. ISME J 7:1016 –1025. https://doi.org/10.1038/ismej.2012.174. 31. Moutsopoulos NM, Chalmers NI, Barb JJ, Abusleme L, Greenwell-Wild T,
13. Camelo-Castillo AJ, Mira A, Pico A, Nibali L, Henderson B, Donos N, Dutzan N, Paster BJ, Munson PJ, Fine DH, Uzel G, Holland SM. 2015.
Tomas I. 2015. Subgingival microbiota in health compared to periodon- Subgingival microbial communities in leukocyte adhesion deficiency
titis and the influence of smoking. Front Microbiol 6:119. https://doi.org/ and their relationship with local immunopathology. PLoS Pathog 11:
10.3389/fmicb.2015.00119. e1004698. https://doi.org/10.1371/journal.ppat.1004698.
14. Griffen AL, Beall CJ, Campbell JH, Firestone ND, Kumar PS, Yang ZK, 32. Paster BJ, Boches SK, Galvin JL, Ericson RE, Lau CN, Levanos VA, Sahas-
Podar M, Leys EJ. 2012. Distinct and complex bacterial profiles in human rabudhe A, Dewhirst FE. 2001. Bacterial diversity in human subgingival
periodontitis and health revealed by 16S pyrosequencing. ISME J plaque. J Bacteriol 183:3770 –3783. https://doi.org/10.1128/JB.183.12
6:1176 –1185. https://doi.org/10.1038/ismej.2011.191. .3770-3783.2001.
15. Socransky SS, Haffajee AD, Smith C, Dibart S. 1991. Relation of counts of 33. Ling Z, Liu X, Luo Y, Yuan L, Nelson KE, Wang Y, Xiang C, Li L. 2013.
microbial species to clinical status at the sampled site. J Clin Periodontol Pyrosequencing analysis of the human microbiota of healthy Chinese
18:766 –775. https://doi.org/10.1111/j.1600-051X.1991.tb00070.x. undergraduates. BMC Genomics 14:390. https://doi.org/10.1186/1471
16. Kirst ME, Li EC, Alfant B, Chi YY, Walker C, Magnusson I, Wang GP. 2015. -2164-14-390.
Dysbiosis and alterations in predicted functions of the subgingival 34. Laksmana T, Kittichotirat W, Huang Y, Chen W, Jorgensen M, Bumgarner
microbiome in chronic periodontitis. Appl Environ Microbiol 81:783–793. R, Chen C. 2012. Metagenomic analysis of subgingival microbiota fol-
https://doi.org/10.1128/AEM.02712-14. lowing non-surgical periodontal therapy: a pilot study. Open Dent J
17. Ning J, Beiko RG. 2015. Phylogenetic approaches to microbial commu- 6:255–261. https://doi.org/10.2174/1874210601206010255.
nity classification. Microbiome 3:47. https://doi.org/10.1186/s40168-015 35. Zorina OA, Petrukhina NB, Basova AA, Shibaeva AV, Trubnikova EV, Shevelev
-0114-5. AB. 2014. Identification of key markers of normal and pathogenic microbi-
18. Shade A, Handelsman J. 2012. Beyond the Venn diagram: the hunt for a ota determining health of periodontium by NGS-sequencing 16S-rDNA
core microbiome. Environ Microbiol 14:4 –12. https://doi.org/10.1111/j libraries of periodontal swabs. Stomatologiia (Mosk) 93:25–31. https://doi
.1462-2920.2011.02585.x. .org/10.17116/stomat201493625-31. (In Russian.)
19. Jalanka-Tuovinen J, Salonen A, Nikkila J, Immonen O, Kekkonen R, Lahti 36. Teles R, Sakellari D, Teles F, Konstantinidis A, Kent R, Socransky S,
L, Palva A, de Vos WM. 2011. Intestinal microbiota in healthy adults: Haffajee A. 2010. Relationships among gingival crevicular fluid biomark-
temporal analysis reveals individual and common core and relation to ers, clinical parameters of periodontal disease, and the subgingival

July 2017 Volume 83 Issue 14 e00462-17 aem.asm.org 12

Dysbiosis in Periodontitis Applied and Environmental Microbiology

microbiota. J Periodontol 81:89 –98. https://doi.org/10.1902/jop.2009 Biogeography of a human oral microbiome at the micron scale. Proc
.090397. Natl Acad Sci U S A 113:E791–E800. https://doi.org/10.1073/pnas
37. Zhou Y, Mihindukulasuriya KA, Gao H, La Rosa PS, Wylie KM, Martin JC, .1522149113.
Kota K, Shannon WD, Mitreva M, Sodergren E, Weinstock GM. 2014. 41. Zhou M, Rong R, Munro D, Zhu C, Gao X, Zhang Q, Dong Q. 2013.
Exploration of bacterial community classes in major human habitats. Investigation of the effect of type 2 diabetes mellitus on subgingival
Genome Biol 15:R66. https://doi.org/10.1186/gb-2014-15-5-r66. plaque microbiota by high-throughput 16S rDNA pyrosequencing. PLoS
38. Zijnge V, van Leeuwen MB, Degener JE, Abbas F, Thurnheer T, Gmur R, One 8:e61516. https://doi.org/10.1371/journal.pone.0061516.
Harmsen HJ. 2010. Oral biofilm architecture on natural teeth. PLoS One 42. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N,
5:e9321. https://doi.org/10.1371/journal.pone.0009321. Schwikowski B, Ideker T. 2003. Cytoscape: a software environment for
39. Kigure T, Saito A, Seida K, Yamada S, Ishihara K, Okuda K. 1995. Distri- integrated models of biomolecular interaction networks. Genome Res
bution of Porphyromonas gingivalis and Treponema denticola in human 13:2498 –2504. https://doi.org/10.1101/gr.1239303.
subgingival plaque at different periodontal pocket depths examined by 43. Faust K, Sathirapongsasuti JF, Izard J, Segata N, Gevers D, Raes J,
immunohistochemical methods. J Periodont Res 30:332–341. https://doi Huttenhower C. 2012. Microbial co-occurrence relationships in the hu-
.org/10.1111/j.1600-0765.1995.tb01284.x. man microbiome. PLoS Comput Biol 8:e1002606. https://doi.org/10
40. Mark Welch JL, Rossetti BJ, Rieken CW, Dewhirst FE, Borisy GG. 2016. .1371/journal.pcbi.1002606.

Downloaded from http://aem.asm.org/ on July 18, 2020 by guest

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