Group Title: BMC Genomics
Title: The Degree of microbiome complexity influences the epithelial response to infection
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Title: The Degree of microbiome complexity influences the epithelial response to infection
Physical Description: Book
Language: English
Creator: Mans, Jeffrey
von Lackum, Kate
Dorsey, Cassandra
Willis, Shaun
Wallet, Shannon
Baker, Henry
Lamont, Richard
Handfield, Martin
Publisher: BMC Genomics
Publication Date: 2009
Abstract: BACKGROUND:The human microflora is known to be extremely complex, yet most pathogenesis research is conducted in mono-species models of infection. Consequently, it remains unclear whether the level of complexity of a host's indigenous flora can affect the virulence potential of pathogenic species. Furthermore, it remains unclear whether the colonization by commensal species affects a host cell's response to pathogenic species beyond the direct physical saturation of surface receptors, the sequestration of nutrients, the modulation of the physico-chemical environment in the oral cavity, or the production of bacteriocins. Using oral epithelial cells as a model, we hypothesized that the virulence of pathogenic species may vary depending on the complexity of the flora that interacts with host cells.RESULTS:This is the first report that determines the global epithelial transcriptional response to co-culture with defined complex microbiota. In our model, human immortalized gingival keratinocytes (HIGK) were infected with mono- and mixed cultures of commensal and pathogenic species. The global transcriptional response of infected cells was validated and confirmed phenotypically. In our model, commensal species were able to modulate the expression of host genes with a broad diversity of physiological functions and antagonize the effect of pathogenic species at the cellular level. Unexpectedly, the inhibitory effect of commensal species was not correlated with its ability to inhibit adhesion or invasion by pathogenic species.CONCLUSION:Studying the global transcriptome of epithelial cells to single and complex microbial challenges offers clues towards a better understanding of how bacteria-bacteria interactions and bacteria-host interactions impact the overall host response. This work provides evidence that the degree of complexity of a mixed microbiota does influence the transcriptional response to infection of host epithelial cells, and challenges the current dogma regarding the potential versus the actual pathogenicity of bacterial species. These findings support the concept that members of the commensal oral flora have evolved cellular mechanisms that directly modulate the host cell's response to pathogenic species and dampen their relative pathogenicity.
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BMC Genomics

Bio.II Central

Research article

The degree of microbiome complexity influences the
epithelial response to infection
Jeffrey J Mansti, Kate von Lackumtl, Cassandra Dorsey', Shaun Willis1,
Shannon M Wallet1,2, Henry V Baker3, Richard J Lamont' and
Martin Handfield*1

Address: 'Department of Oral Biology, College of Dentistry, University of Florida, Gainesville, Florida, 2Department of Periodontology, College
of Dentistry, University of Florida, Gainesville, Florida and 3Department of Molecular Genetics and Microbiology, College of Medicine, University
of Florida, Gainesville, Florida
Email: Jeffrey J Mans; Kate von Lackum; Cassandra Dorsey;
Shaun Willis; Shannon M Wallet; Henry V Baker;
Richard J Lamont; Martin Handfield*
* Corresponding author tEqual contributors

Published: 18 August 2009
BMC Genomics 2009, 10:380 doi: 10.1 186/1471-2164-10-380

Received: 13 November 2008
Accepted: 18 August 2009

This article is available from:
2009 Mans et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: The human microflora is known to be extremely complex, yet most pathogenesis research
is conducted in mono-species models of infection. Consequently, it remains unclear whether the level of
complexity of a host's indigenous flora can affect the virulence potential of pathogenic species.
Furthermore, it remains unclear whether the colonization by commensal species affects a host cell's
response to pathogenic species beyond the direct physical saturation of surface receptors, the
sequestration of nutrients, the modulation of the physico-chemical environment in the oral cavity, or the
production of bacteriocins. Using oral epithelial cells as a model, we hypothesized that the virulence of
pathogenic species may vary depending on the complexity of the flora that interacts with host cells.
Results: This is the first report that determines the global epithelial transcriptional response to co-culture
with defined complex microbiota. In our model, human immortalized gingival keratinocytes (HIGK) were
infected with mono- and mixed cultures of commensal and pathogenic species. The global transcriptional
response of infected cells was validated and confirmed phenotypically. In our model, commensal species
were able to modulate the expression of host genes with a broad diversity of physiological functions and
antagonize the effect of pathogenic species at the cellular level. Unexpectedly, the inhibitory effect of
commensal species was not correlated with its ability to inhibit adhesion or invasion by pathogenic species.
Conclusion: Studying the global transcriptome of epithelial cells to single and complex microbial
challenges offers clues towards a better understanding of how bacteria-bacteria interactions and bacteria-
host interactions impact the overall host response. This work provides evidence that the degree of
complexity of a mixed microbiota does influence the transcriptional response to infection of host epithelial
cells, and challenges the current dogma regarding the potential versus the actual pathogenicity of bacterial
species. These findings support the concept that members of the commensal oral flora have evolved
cellular mechanisms that directly modulate the host cell's response to pathogenic species and dampen their
relative pathogenicity.

Page 1 of 13
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BMC Genomics 2009, 10:380

The human microflora is an extremely complex ecosystem
characterized by the simultaneous presence of a large
number of "normal" colonizers, associated with health,
and thriving in a dynamic environment alongside oppor-
tunistic and pathogenic species. Since health is the most
common state of a host, it has been speculated that the
autochthonous flora has co-evolved with its host to inter-
act in a balanced state that is beneficial to both the host
and the microflora. There are an appreciable number of
benefits to the host that the indigenous flora is thought to
provide, including the synthesis of vitamins (B complex
and K), the prevention of infection by pathogens (by
direct competition for niches or by immune cross-reactiv-
ity), and its impact in the normal development of the
immune system [1,2]. Indeed, observed differences
between pathogen-stimulated and commensal-stimulated
immune responses are suggested to provide important
insights toward understanding the molecular aspects of
host-microbiota interactions [2-4]. Most notable exam-
ples include the intestinal commensals whose interac-
tions with the gut epithelium trigger both innate and
adaptive immune responses, influence epithelial cell pro-
liferation [5], and induce commensal-specific IgA produc-
tion and secretion in the gut to keep commensal species in
check [6]. More recently, an oral commensal organism,
Streptococcus salivarius strain K12, was demonstrated to
antagonize Pseudomonas aeruginosa-induced IL-8 secretion
from human bronchial epithelial cells, suggesting a role
for commensal species in modulating human epithelial
cell immune responses in the nasopharynx [4]. Likewise,
a separate group has described the capability of S. cristatus
and certain other streptococcal species to dampen the IL-
8 response induced by infection with the periodontal
pathogen Fusobacterium nucleatum in four different epithe-
lial cell lines. These observations demonstrate that pol-
ymicrobial infection of epithelial cells with oral
streptococcal species and commonly associating patho-
gens can attenuate the proinflammatory effects elicited
during infection of these cells with the pathogens [7].

Since host and microbiota interactions are inherently
unstable, disease may arise at the mucosal surface of a sus-
ceptible host when a perturbation occurs in the epithelial
environment leading to "unintended" (in an evolutionary
sense) consequences of immune or other host cell activity.
Such instances include both hyper-acute immune
responses as well as the converse situation, such as when
the host becomes immuno-compromised. Further, the
complex etiology of oral infectious diseases involves con-
sortia of bacteria thriving in biofilms and working in con-
cert with immunological susceptibilities in the host. In
particular, periodontal diseases are a group of infections
that lead to damage of the periodontium and ultimately,
exfoliation of the teeth, and these infections are among


the most common bacterial diseases of humans [8,9]. As
complex as these multifactorial inflammatory diseases
are, there is a consistent relationship between the Gram-
negative anaerobe Porphyromonas gingivalis and severe,
chronic manifestations of the disease [8-11]. However,
while bacteria have traditionally been viewed as strictly
beneficial or harmful, it is our contention that these over-
simplified designations are no longer appropriate, and
that expression of an organism's potential pathogenicity is
context dependent [12]. The current genomic revolution
offers an unprecedented opportunity to identify the
molecular foundations of host-commensal and host-
pathogen relationships so that we can understand how
they assist or interfere with our normal physiology [2]. In
line with a broader contextual view of relative potential
pathogenicity, transcriptional profiling has emerged as a
tool that allows the host to report the level of disruption
induced by bacteria in the absence of preconceived
notions regarding bacterial "intentions" (reviewed in

The initial interface between periodontopathic organisms,
such as P. gingivalis, or members of the normal flora such
a Streptococcus gordonii and the host is the epithelial layer
in the subgingival crevice. Epithelial cells have tradition-
ally been considered a passive barrier to infection. There
is, however, growing evidence that they contribute more
significantly to host defense than previously appreciated.
There is a significant body of literature that reports that
these cells efficiently signal a microbial intrusion to the
immune cells to ensure effective mobilization of the
innate and specific defense mechanisms. Furthermore epi-
thelial cells can produce oxidants and antimicrobial pep-
tides to actively participate in fending off intruding
microbes [14,15]. Consequently, several organisms have
evolved to circumvent the specific defenses available to
the epithelium. For example, P. gingivalis invades epithe-
lial cells and remains viable intracellularly in a rather cov-
ert manner whereby host cell programmed cell death is
suppressed [16]. P. gingivalis stimulates integrin-depend-
ent signaling in host cells to effect invasion and subse-
quently resides in the perinuclear area in epithelial cells
[17]. In fact, besides P. gingivalis, an intracellular location
has been suggested to be a natural component of the life-
style of a number of other oral organisms [18-20]. Conse-
quently, the regulation of normal processes such as cell
division or apoptosis may be key events to a balanced
longstanding intracellular state whereby microbes of the
oral cavity and host cells co-exist and inflict a minimal
degree of harm on each other.

In regards to the extracellular source for this pathogen,
numerous studies demonstrate that P. gingivalis aggregates
with common members of supragingival plaque includ-
ing the periodontal pathogen Aggregatibacter actinomyce-

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BMC Genomics 2009, 10:380

temcomitans, commonly associated with Localized
Aggressive Periodontitis (LAP), and numerous oral strep-
tococci ubiquitous to early plaque biofilms [21-23]. Inter-
estingly, P. gingivalis only forms biofilms with S. gordonii
and other oral streptococci but not S. mutans species
[24,25], the specific molecular adherence strategies of
which are well documented [24,26-29]. Recently, it was
discovered that approximately 1% to 2% of the P. gingiva-
lis genome was regulated during the initial stages of devel-
opment of a community with S. gordonii and contribute to
the development of heterotypic biofilms [26]. Pioneering
organisms and their interspecies interactions thus ulti-
mately determine the diversity potential of heterotrophic
biofilms developing on newly acquired tooth pellicle.
One cannot consider the pathogenicity of single perio-
dontal organisms without considering the biofilm context
that brought them to the host interface. The encounter
between host and microbiota may thus be a finely tuned
set of interactions whereby both cell types co-exist with
each other. Hence, the transcriptional status of gingival
cells has significance for the specific response to individ-
ual organisms and to overall well-being of the host
(reviewed in [12,13]).

This is the first report that determines the epithelial tran-
scriptional response to co-culture with complex microbi-
ota constituted with P. gingivalis and S. gordonii, two
interacting co-inhabitants of the oral cavity. Colonization
of the oral cavity with S. gordonii and related streptococci
is one of the first steps in the development of plaque upon
the acquired pellicle of a recently cleaned tooth structure.
P. gingivalis, a later colonizing oral inhabitant, is associ-
ated with chronic gingival disease and can result in severe
periodontal tissue destruction. The host response to oral
colonization has both pathophysiological and temporal
effects on disease progression induced by late colonizing
pathogenic species. It is often the balance between com-
mensal, opportunistic, and pathogenic species in a partic-
ular microbiota that can tip the scales in favor of
periodontal health or disease [30]. Studying the patterns
of gene expression induced by co-infection with a com-
plex microbiota can help reflect a more biologically and
physiologically relevant host-pathogen interplay. We
investigated whether the presence of an oral commensal
in co-culture with gingival epithelial cells and P. gingivalis
in a mixed microbial challenge would impact the epithe-
lial response. The data presented herein supported the
concept that S. gordonii does impart profound and global
transcriptional changes in the epithelial response to a P.
gingivalis challenge. In addition, colonization with com-
mensal species directly modulated the host cell's response
to co-infection with a pathogenic species and antagonized
the ability of P. gingivalis to modulate cell cycle, yet had
little effect of the invasiveness of this pathogenic species.


Impact of Co-Culture on Adhesion and Invasion Levels of
Bacteria to HIGK
Many studies have shown that S. gordonii and P. gingivalis
demonstrate vastly different interaction characteristics
with gingival epithelial cells. The former aggregates on the
outside of epithelial cells and the latter invades within
twenty minutes of infection, and resides in a perinuclear
localization [31-33]. The interaction characteristics of P.
gingivalis with HIGK in mono- and mixed cultures were
first investigated to establish the baseline of this system.
Consistent with previous reports, at a Multiplicity of
Infection (MOI) of 2500:1 and in mono-infection, a great
majority of the S. gordonii remained extracellular and
interacted to a total level of 20 1 colony forming units
(CFU) per HIGK cell. In contrast, P. gingivalis was recov-
ered mostly intracellularly under the conditions used in
this study, and at a level of approximately 25 1 CFU per
HIGK cell. To determine whether the level of invasion by
P. gingivalis was impacted by co-culture in the presence of
commensal species, antibiotic protection assays were con-
ducted on cells co-infected with P. gingivalis and S. gordo-
nii using the same infection parameters as in the mono-
species infection experiments. Although invasion levels of
P. gingivalis were reduced by co-infection with S. gordonii,
the effect was not statistically significant. Similarly, the
levels of adhesion of S. gordonii were not significantly
impacted by co-culture with P. gingivalis (data not
shown). Under the experimental conditions used herein,
the HIGK monolayer remained of a normal appearance
and morphology and did not appear to be affected by the
co-culture with either of the bacterial species at 2 h post-
infection. Microscopic observations supported the finding
that co-culture with a complex flora, neither abrogated
nor potentiated the invasiveness of P. gingivalis (data not
shown). Hence, bacteria-bacteria interactions that occur
in this complex flora model and under the conditions
used herein did not significantly impact the invasive phe-
notype of P. gingivalis.

A Complex Infection Elicits a Different Epithelial
Assessment of the HIGK transcriptional response during
interaction with P. gingivalis, S. gordonii, or a mixture of
the two organisms was performed to determine the global
impact of a mixed microbial challenge on epithelial cells.
Signal intensity data for the probe sets were categorized by
unsupervised analysis using Cluster [34] and supervised
class prediction with BRB Array Tools [35]. Relative inten-
sity values were displayed using Treeview [34]. The unsu-
pervised hierarchical clustering analysis showed that the
biological replicates clustered together (data not shown).
This demonstrated that each treatment condition gener-
ated a unique transcriptional response in HIGK that was

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consistent and reproducible amongst biological repli-
cates. Supervised class prediction utilizing a combination
of several prediction methods and a random variance
model for univariate F tests [35] revealed 6066 probe sets
were differentially expressed among the treatment condi-
tions, at a level of significance of p < 0.05. Figure 1 shows
a Treeview representation of the 6066 probe sets differen-
tially expressed amongst the four treatments. A two hour
mono-infection with S. gordonii and P. gingivalis alone
demonstrated unique transcriptional responses that were
diametrically opposite to one another, as well as distinct
when compared to uninfected HIGK. At this early infec-
tion time point, the HIGK's transcriptional response to
mixed infection appears most similar to the transcrip-
tional pattern of the HIGK infected with S. gordonii alone.
These data suggested that, under mixed infection condi-
tions, the commensal species had the ability to inhibit
and reprogram the transcriptional response elicited by P.
gingivalis. The antagonistic effect of S. gordonii on the tran-
scriptional response to cells co-infected with P. gingivalis
could not be ascribed to different invasion levels of P. gin-
givalis in mixed challenges.

While mixed co-cultures of pathogen with commensal
species clustered closer to the signatures associated with S.
gordonii alone, ontology analysis was undertaken to eluci-
date the biological relevance of the cellular pathways most
differentially impacted by a mixed infection condition.
The 6066 probe sets that were differentially impacted at a
significance level of p < 0.05 amongst the infection condi-
tions were used to populate known KEGG pathways using
Pathway Express software [36-39]. One feature of this
software is the use of statistical algorithms to determine
the most highly impacted pathways, considering the
number of input genes compared to total genes in a given
pathway; a commonly used hypergeometric over-repre-
sentation approach. The most highly impacted pathways
(p < 0.05) populated by genes differentially expressed
(also p < 0.05) are listed in Table 1. As it was the most sig-
nificantly impacted pathway in this analysis (p < 0.001),
the cell-cycle pathway was selected for further phenotypic
validation. As indicated in Table 1, a total of 45 cell cycle-
associated genes were differentially modulated by a com-
plex flora as compared to mono-infection with P. gingiva-
lis. A significant proportion of two key classes of
regulatory molecule genes cyclins and their cognate cyc-
lin-dependent kinases (CDK) were differentially regu-
lated in HIGK under each infection condition. Cyclins are
transiently expressed nuclear proteins that are required for
CDK activation that drives progression through a cell cycle
[40]. A summary of the major cell cycle regulatory mole-
cules impacted by infection with P. gingivalis and S. gordo-
nii is presented in Figure 2.

- 00
- 10


Figure I
HIGK Gene expression upon P. gingivalis and S. gordo-
nii single- and mixed infections. Hierarchical clustering
was performed on variance-normalized signal of gene expres-
sion data from uninfected HIGK cells (CTRL) and from cells
in co-culture with either organism or a mixture of both for 2
h before RNA isolation and purification. Probe set signal
intensities were variance-normalized, mean-centered across
samples, and subjected to hierarchical cluster analysis. Heat
map and dendrogram were constructed from 6066 probe
sets that were differentially expressed among the treatment
conditions at a level of significance of p < 0.05. The degree of
similarity between the transcriptional profiles of each sample
is expressed by Pearson's correlation coefficient distance
metric, according to the adjacent scale. The expression state
of each data point is represented as standard deviations from
the mean expression level for that gene in all samples. Red
indicates a relative increase, green indicates a relative
decrease, and black indicates no relative change of mRNA
transcripts for a given gene.

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BMC Genomics 2009,10:380


Table I: HIGK gene ontology comparing P. gingivalis and S. gordonii co-infectionto P. gingivalis mono-infection.a

Impact factorc

Cell cycle
Ubiquitin mediated proteolysis
Adherens junction
TGF-beta signaling pathway
SNARE interactions in vesicular transport
Jak-STAT signaling pathway
MAPK signaling pathway
Wnt signaling pathway
Colorectal cancer
Notch signaling pathway


# of modulated genes (total) in Pathwayd

45 (1 12)
22 (45)
29 (77)
29 (84)
16 (36)
30 (84)
75 (273)
23 (77)
16 (46)

aThe epithelial cell pathways were determined by Pathway Express.
bKyoto Encyclopedia of Genes and Genomes pathways http://www.genome.ip/kegg/.
cThe impact factor measures the pathways most affected by the changes in gene expression by considering the proportion of differentially regulated
genes, the perturbation factors of all the pathway genes, and the propagation of these perturbations throughout the pathway. Only pathways with
an impact factor greater than 5 are included.
dNumber of regulated genes in a pathway/total number of genes currently mapped to this pathway.

Effect of a Mixed Flora Challenge on the Epithelial Cell
Growth Dynamic
The finding that cell cycle regulation was significantly
impacted in HIGK cells encountering mono- versus mixed
infection was confirmed at the cellular level using two-
color fluorescence-activated cell sorting (FACS) cytomet-
ric analysis. The percentage of HIGK in any given phase of
the cell cycle was analyzed using HIGK co-cultured with P.
gingivalis and S. gordonii in mono- and mixed-cultures in
the presence of BrdU (Figure 3). The ratios of BrdU
amounts incorporated during DNA replication of S phase,
compared to total DNA content and overall cell size,
reflects the cell cycle phase of a given cell. A minimum of
1 x 104 cells were analyzed in each condition to determine
the proportion of HIGK in different cell cycle phases (Fig-
ure 3, panel G). The greatest differences infection status
imparted upon HIGK cell cycling were observed during S
phase, which was consistent with the mRNA profiling
where high proportion of S-phase promoting factor genes
(Figure 3) were impacted by infection with a mixture of S.
gordonii and P. gingivalis, compared to mono-infection
with pathogen alone. Infection with S. gordonii resulted in
the majority of HIGK remaining in S phase while infec-
tion with P. gingivalis resulted in a wider distribution of
HIGK in each of the various stages of cell cycling, mirror-
ing uninfected controls. Introduction of the commensal S.
gordonii to P. gingivalis-infected HIGK cells promoted pro-
gression of HIGK into S-phase of the cell-cycle, shifting
the cycling trend towards that of cells challenged only
with the commensal species (Figure 3). What remained
consistent amongst the microarray and phenotypic exper-
iments was that HIGK infected with a mixture of S. gordo-
nii and P. gingivalis cycled similarly to cells mono-infected
with S. gordonii.

FACS analysis confirmed microscopic observations and
CFU data on P. gingivalis interaction characteristics with
HIGK. As shown in Figure 3 (blue dots), the level of total
interaction was similar (p = 0.106) whether P. gingivalis
was in single (B) or in mixed (D) co-cultures with HIGK.
Thus, both the total numbers of Pg interacting per cell
(Figure 4A), as well as the total numbers of HIGK that
interact with at least a single Pg bacterium (Figure 3B, D)
were both not significantly influenced by Sg coinfection
compared to mono- Pg infection. This corroborates the
notion that the observed shift in cycling behavior of HIGK
co-cultured with P. gingivalis and S. gordonii compared to
that of HIGK infected with P. gingivalis alone was not due
to the inhibition of P. gingivalis adhesion or invasion.
Transcriptionally at 2-hours, and phenotypically at 24 h
post-infection, commensal species in a mixed infection
can thus influence HIGK's physiological response towards
one that is characteristic of HIGK cells mono-infected
with a commensal species.

To reconcile the transcriptional snap-shot and the cell-
cycle analysis into a coherent model, a direct measure-
ment of cell division time was performed over a period of
six days after a 2 h infection. As shown in Figure 4B,
mono-infection with P. gingivalis stimulated an HIGK
growth rate resembling that of uninfected controls (p >
0.05). In contrast, mono-infection with S. gordonii con-
strained growth of HIGK as compared to non-infected
controls (p < 0.05). Mixed infection of HIGK with S. gor-
donii and P. gingivalis resulted in a dose-dependant inhibi-
tion of cell growth where S. gordonii antagonized the
growth stimulatory effect of P. gingivalis at the lowest MOI
tested (100:1), but seen with more significance at higher
MOIs (500:1) (2500:1) (p < 0.05). The constrained
growth observed under S. gordonii infection conditions

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Impacted pathwayb


< 0.001
< 0.00 I1
< 0.00 I1

BMC Genomics 2009,10:380


Ot aSe~tW~l GOmVhfelt DNA damaati pontI

1/ .

GI __ G2
plg p27I I -i

.---- ____ ._ __ ....


Impact of a mixed microbial challenge on cell cycle control in HIGK. Transcriptional regulation of D the KEGG ckell

(page number not for citation purposes)
1-4, -- Fe-P*APKI

BMC Genomics 2009,10:380




CellTr'ckerGre n CI llTrackerBlue

G 80 -
70 Pg #
4.60 Sg
0 D0 Pg+Sg *
E3 uninfected
30 -
20 .

sub GOGI GO/G G2+ M S phase

Figure 3
Infection with S. gordonii antagonizes the effect of P. gingivalis on the cell cycle. HIGK (uninfected in A) were simul-
taneously grown in the presence of BrdU and infected with P. gingivalis labeled with CellTracker Green BIODIPY shown in blue
(B), CellTracker Blue CMAC labeled S. gordonii shown in pink (C), or a combination of P. gingivalis and S. gordonii for 4 h (D).
HIGK cells were treated with antibiotic to kill extracellular bacteria and were cultured for an additional 20 h prior to harvest-
ing for FACS analysis. The cell cycle positions and active DNA synthetic activities of cells were determined by analyzing the
correlated expression of total DNA (7-AAD) and incorporated BrdU levels. Uninfected control HIGK were included for com-
parison of baseline cycling patterns. Data represent duplicate experiments. Region gate I, HIGK were apoptotic (defined as sub
G0o/G); region 2, Go/G1; region 3, S phase; region 4, G2+M. Panel E and F demonstrate the gated HIGK populations used
throughout all the analyses for CellTracker Green labeled P. gingivalis and CellTracker Blue CMAC labeled S. gordonii, respec-
tively. Panel G depicts quantitative analysis of HIGK cell cycling in response to mono- and mixed infections. Results are the
mean of three experiments. Statistical analysis was conducted using an ANOVA with Bonferroni's Multiple Comparison Test. *
Pg vs. Sg sub GO/G Pg vs. Sg GO/G1, Pg vs. Sg G2+M, Pg vs. Sg S phase, p value < 0.05. # Pg vs. Sg+Pg sub Go/G1, Go/G1, G2+M,
S phase, p values < 0.001. Error bars represent the mean SD.

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BMC Genomics 2009, 10:380

was not due to indirect effects, such as pH changes or
nutrient depletion of the growth media, but are the result
of direct interaction between S. gordonii and HIGK cells
because conditioned S. gordonii media had no effect on
HIGK proliferation (see Additional file 1, Additional file
2, and Additional file 3).

Previous studies have elucidated the unique transcrip-
tional signatures that are elicited in response to mono-
infection with oral pathogens P. gingivalis and A. actino-
mycetemcomitans [41] as well as opportunistic bacteria
such as Fusobacterium nucleatum and the commensal spe-
cies including S. gordonii [42]. Transcriptional responses
to P. gingivalis or A. actinomycetemcomitans mono-infec-
tions demonstrated organism-specific responses that are
drastically different [41]. In contrast, the opportunistic
pathogen Fusobacterium nucleatum and S. gordonii elicit
profiles that are more transcriptionally restrained in com-
parison to those characterizing the overt periopathogens
[42]. Interestingly, the transcriptional changes that have
been observed in HIGK infected with different commen-
sal or pathogenic species does not correlate with the levels
of invasiveness that are characteristic of these bacterial
species. For example, F. nucleatum invades epithelial cells,
while S. gordonii does not. Yet, both species induce very
similar transcriptional responses in HIGK [42]. This con-
siderable overlap suggests an evolutionary-driven pro-
grammed response to the presence of indigenous
constituents of the normal human oral flora. Hence, gin-
gival epithelial cells can differentiate between commensal
or potentially pathogenic species, regardless of their cellu-
lar localization, and respond accordingly. In this study,
the dichotomy in the responses to pathogen or commen-
sal supports previous reports that suggested a species-spe-
cific recognition that was tailored to each different
bacterial species [41,42].

The microbial challenge faced by the subgingival area is
one of great complexity and is dynamic in status. The host
epithelial response to in vivo oral biofilms would be
extremely difficult to mimic, as an in vitro biofilm model
may not exactly duplicate the complex microbiome
present in the oral cavity. Furthermore, the strata of organ-
isms present in oral biofilms/plaque vary widely amongst
individuals [43]. As such, there are few studies that have
investigated bacterial pathogenicity in complex infection
models. Nevertheless, the current work offers proof-of-
principle that an admittedly overly simplified mixed
microflora can have profound effects on the host tran-
scriptome, as compared to mono-species bacterial chal-
lenges. To date, this is the first characterization of the
epithelial response to a mixed infection encompassing
commensal and pathogenic organisms. When S. gordonii
and P. gingivalis were co-cultured together with HIGK, the


resulting transcriptional response of the host cell was
most similar to that elicited by infection with the S. gordo-
nii alone. This finding supports the hypothesis that com-
mensal species modulate the pathogenicity of P. gingivalis
in vivo. This result was not surprising considering that
microbe-microbe interactions between P. gingivalis and S.
gordonii modulate the gene expression pattern of P. gingi-
valis [26].

In our model, the most significant effect of a mixed chal-
lenge was the impact on host cell cycling. HIGK infected
with P. gingivalis were more evenly distributed in all
phases of the cell cycle compared to cells infected with
both P. gingivalis and S. gordonii. HIGK exposed to this
mixed co-culture infection, as well as S. gordonii alone
were more prevalently found in Synthesis phase. Whether
this is a true stage arrest phenomenon elicited by the pres-
ence of S. gordonii could not be determined since cells
were not a priori synchronized. Nevertheless, cell growth
analysis corroborated the ability of P. gingivalis to induce
cellular proliferation as compared to uninfected controls.
Further, mixed infections with S. gordonii were capable of
inhibiting the cellular proliferation induced by P. gingiva-
lis in a dose-dependant fashion.

Our microarray analyses are in line with previous pro-
teomic experiments performed in primary gingival epithe-
lial cells (GEC) [44]. Both studies found that several
pathways exerting regulatory control over the cell cycle
were impacted by P. gingivalis mono-infection. Both stud-
ies also consistently showed a regulation of Cdk2 and
Cdk4/6 upon infection of epithelial cells with P. gingivalis.
Both Cdk2 and Cyclin D were down-regulated in response
to infection with P. gingivalis with respect to transcript lev-
els and protein levels. Conversely, transcript levels were
increased in response to a mixed infection with S. gordonii.
Only by combining genomic, proteomic, and phenotypic
epithelial responses can a true picture emerge of what
impact the increasing complexity of a microbiome has on
host cellular responses.

Recent evidence supports the concept that cell cycle is sig-
nificantly impacted in diseased sites as compared to
matching healthy sites in periodontitis and gingivitis
patients [45]. Although arguably more clinically relevant,
studies involving human specimens or primary cell cul-
ture present the greatest potential for uncontrolled exper-
imental variables. Examples include the genetic variability
between donors, different levels of inflammation and age
of participant, diet, diurnal variations in gene expression,
type of anesthesia used, length of ischemia prior to tissue
removal, time from tissue removal to RNA stabilization,
and other confounding factors [12]. In contrast, experi-
ments performed with immortalized cell cultures -
although simplistic models of the in vivo environment -

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BMC Genomics 2009, 10:380


O -------------------

0 --
Pg mono-infected Mixed culture with

I I,*




- Pg 100:1

- Sg2500:1
- MIXED 100:100:1

I MIXED 500:100:1 MIXED 2500:100:1

Figure 4
Accelerated HIGK proliferation upon infection with
P. gingivalis is inhibited in co-culture with S. gordonii.
A) Total interaction and invasion levels determined by meas-
uring the total numbers of P. gingivalis associated with HIGK
cells by live counts in mono-(Pg) and in mixed infection
(Mixed) with S. gordonii at the highest MOI. Results are the
mean of three experiments. B) HIGK cells at a low conflu-
ency were co-cultured with single and complex mixtures of
bacteria and cultured for up to 144 hours. Labels: Control,
uninfected HIGK; Sg, S. gordonii; Pg, P. gingivalis; MIXED, co-
infection with S. gordonii and P. gingivalis at different multiplic-
ity of infection (MOI). All cell counts were performed in trip-
licate and all experiments were repeated twice. p > 0.05 P.
gingivalis vs non-infected controls; #p < 0.05 S. gordonii vs.
non-infected controls; A^p < 0.05 S. gordonii co-infection with
P. gingivalis (2500:1) vs. non-infected controls (p < 0.05) by
ANOVA with Dunnett's Multiple Comparison Test.

present a significantly higher degree of stability and can be
manipulated. This translates into a dataset that is less
noisy and ultimately presents a greater potential to dissect
a given pathway and predict a phenotype with biological
relevance. Hence, the current transcriptional dataset pro-

vided valuable insight on the intricate and complex mech-
anisms that may be responsible for the differential cell
cycle effect of P. gingivalis and S. gordonii, and provided
clues on how the presence of the latter in a mixed infec-
tion may affect the proliferative properties of P. gingivalis.
For example, CycD, CDK4 and CDK6, were upregulated
in HIGK infected with the mixed microflora compared to
HIGK transcript levels in response to mono-infection with
P. gingivalis. At the beginning of the cell cycle, Cyclin D
and CDK4 and 6 form complexes in response to extracel-
lular signals for growth and stimulate entry into G1. The
same pattern held for CDK2 which complexes with several
different cyclins, but when bound to cyclin E pushes the
cell from G, to S phase (G1/S transition). Cyclin A, which
when completed with CDK2 initiates the G2/M transition,
was upregulated in the presence of a mixed infection of
HIGK. Transcript levels of all of these cell cycle regulators
were down regulated in P. gingivalis infected cells com-
pared to uninfected controls. Down regulation of Cyclin
A transcript in P. gingivalis infected HIGK fits with regula-
tion of the rest of the cell cycle modulating factors because
Cyclin A, when completed with CDK1, also functions as
a S-phase promoting factor. Many regulatory steps in pro-
tein production, folding, and function as well as post-
transcriptional kinase activities can also impact protein
function. These post-transcriptional modifications were
not detected by transcriptional profiling, yet may signifi-
cantly alter the initial epithelial transcriptional response
with any bacterium. While the aforementioned cyclins
and CDKs play a large role in determining the stage of cell
cycle progression, ultimately these regulatory molecules
control the activity of the transcriptional activator E2F.
The activation of E2F results in transcription of a number
of genes that promote the cell's transition from G, to S
phase via phosphorylation and deactivation of the Rb
protein. E2F transcript levels were up-regulated in HIGK
infected with both P. gingivalis and S. gordonii and down-
regulated in cells mono-infected with P. gingivalis. CDC6,
which is required for the initiation of DNA replication,
was similarly regulated in both treatment conditions. Fur-
ther validation of the current transcriptional dataset is
ongoing in primary GECs utilizing clinical biofilm speci-

Besides the cell cycle pathway, additional pathways were
differentially impacted by mono- versus mixed infections,
and are currently being further confirmed at the protein
level and phenotypically. These pathways include apopto-
sis and numerous signaling pathways (Table 1). In addi-
tion, genes associated with cancer were differentially
impacted by infection with P. gingivalis, which is not sur-
prising since genes involved in normal physiological func-
tions are often also implicated in cancer when they are
disregulated. It remains unclear and speculative whether
P. gingivalis is directly involved in the initiation or exacer-
bation of carcinogenic lesions or whether this effect is

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BMC Genomics 2009, 10:380

consequent to the attempt of P. gingivalis to establish an
anti-apoptotic phenotype in GECs. This phenomena has
already been shown to help this microorganism to propa-
gate a suitable niche for an extended infection and
involves manipulating pathways that are normally
involved in normal cellular functions as well as in cancer
[16,41,42,46,47]. Hence, further endeavors into the pos-
sible carcinogenic potential of periodontal pathogens and
the dissection of the role of commensal species in affect-
ing these pathways is appropriate and timely.

In summary, the present study provides evidence that the
degree of complexity of a mixed microbiota influences the
transcriptional response to infection of host epithelial
cells. The transcriptional repertoire of genes impacted by
co-infection with S. gordonii and P. gingivalis compared to
that of HIGK infected with P. gingivalis alone demon-
strated that commensal species are able to modulate
expression of host genes with a broad diversity of physio-
logical functions and antagonize the effect of pathogenic
species at the cellular level. This global expression study
provides insight into both host-pathogen interactions
within the context of a complex microbial infection and
challenges the current dogma regarding the potential ver-
sus the actual pathogenicity of an oral species in the con-
text of a complex biofilm. Although S. gordonii is
consistently referred to as a commensal, its association
with the host is not completely inconsequential as it
serves as an early colonizer responsible for the develop-
ment of potentially pathogenic plaque species. The study
of the global transcriptome of epithelial cells to single and
complex microbial challenges offers clues towards a better
understanding of how bacteria-bacteria interactions and
bacteria-host interactions impact the overall host
response. Ultimately, this work could lead to identifying
host responses that can be used to predict a commensal or
pathogenic challenge as well as the overall pathogenic
potential of a mixed microbiota. Genes and cell pathways
impacted by such infections could serve as diagnostic
markers to assess the pathogenicity of individual complex
microbial communities across various populations bat-
tling different manifestations and stages of periodontal

Bacterial strains
Porphyromonas gingivalis strain ATCC 33277 was cultured
anaerobically for 24 h at 37 C in trypticase soy broth sup-
plemented with yeast extract (1 mg mL-1), haemin (5 tig
mL-1), and menadiaone (1 |ig mL-1). Streptococcus gordonii
strain DL1 was cultured aerobically for 24 h at 37C in
Todd Hewlett Broth supplemented with 0.5% yeast
extract and 0.5% glucose.


Eukaryotic cell lines
Human immortalized gingival keratinocytes (HIGK) were
originally generated by transfection of primary GEC with
E6/E7 from HPV [48]. HIGK were cultured under 5% CO2
in keratinocyte serum-free medium (K-SFM, Gibco/Invit-
rogen, Carlsbad, CA) supplemented with 0.05 mM cal-
cium chloride, 200 mM L-glutamine (Gibco/Invitrogen),
and 1% antibiotic/antimycotic (Gibco/Invitrogen).

Microbial-host cell co-culture
Bacteria were harvested by centrifugation and resus-
pended in antibiotic-free K-SFM media. HIGK (107 cells)
were washed three times with phosphate-buffered saline
(PBS) and incubated with bacteria at an MOI of 100 for P.
gingivalis and 2500 for S. gordonii. After 2 h at 37C, the
innoculate was removed and HIGK cells were immedi-
ately lysed with Trizol reagent (Invitrogen, Carlsbad, CA)
before RNA extraction. Co-cultures were carried out in
quadruplicate per infection condition. For microbial-host
cell interaction studies, total numbers of S. gordonii asso-
ciated with HIGK both externally and internally after
2 h incubation and washing were determined by Triton X-
100 lysis and plate counts. Invasion levels of S. gordonii
and P. gingivalis co-cultured with HIGK both individually
and together in a mixed infection were measured by anti-
biotic protection assays as previously described [49]. In
mixed co-culture with HIGK and P. gingivalis mono-infec-
tion, cells were lysed with sterile water as P. gingivalis were
not viable after exposure to Triton X- 100.

RNA isolation, cRNA synthesis and chip hybridization
Total RNA was extracted, DNAse-treated, purified and
quantified according to standard methods (Qiagen and
Affymetrix). Briefly, double stranded cDNA was synthe-
sized using 8 |ig of total cellular template RNA according
to standardized protocol (SuperScript double stranded
cDNA synthesis kit; Invitrogen, Carlsbad, CA). cRNA was
transcribed in vitro, incorporating biotinylated nucle-
otides via a BioArray high-yield RNA transcript labeling
kit (T7) (Enzo Life Sciences, Farmingdale, NY), frag-
mented, and hybridized onto the Affymetrix human
genome U133 Plus 2.0 human microarrays. Each condi-
tion was studied in biological quadruplicate and samples
were not pooled. The microarrays were hybridized for 16
h at 45 o C, stained with phycoerythrin-conjugated strepta-
vidin and washed according to the Affymetrix protocol
(EukGE-WS2v4) using an Affymetrix fluidics station, and
scanned with an Affymetrix scanner.

Microarray data analysis
Microarray data analysis was performed as previously
described [41,42]. Briefly, expression filters were applied
to remove Affymetrix controls and probe-sets whose sig-
nal was undetected across all samples. The signal intensity

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BMC Genomics 2009, 10:380

values of the resulting dataset were variance-normalized,
mean-centered and ranked by their coefficients of varia-
tion. Normalization was performed to give equal weight
to all probe-sets in the analysis, regardless of the order of
magnitude of the raw signal intensity. To reduce the con-
founding effect of background signal variation on the
analysis, only the half of the dataset demonstrating the
most variation across samples was used to perform unsu-
pervised hierarchical cluster analysis using Cluster soft-
ware [34]. The resulting heat-map and cluster
dendrograms were visualized with Treeview software [34]
to reveal the extent of characteristic host cell responses to
each infection state, defined as identical treatments clus-
tering together. Array results have been deposited in the
GEO repository
index.cgi under accession number GSE12121. Following
initial assessment of the host cell response to each condi-
tion, supervised class prediction was performed to investi-
gate differences in gene regulation among experimental
conditions. For this analysis, the raw signal intensities
were log-transformed for all probe-sets that passed the ini-
tial expression filters, and were correlated using BRB Array
Tools [35]. Diagonal linear discriminant analysis, 1- and
3- nearest neighbors, and nearest centroid prediction
methods were used in conjunction with a random vari-
ance model for univariate F tests. In each supervised anal-
ysis, biological replicates were grouped into classes
according to their infection state during co-culture experi-
ments and probe sets significant at the p < 0.05 level
between the class were identified. To test the ability of
these significant probe sets to truly distinguish between
the classes, leave-one-out-cross-validation (LOOCV) stud-
ies were performed for each prediction model. In these
LOOCV studies each array was left out in turn and classi-
fiers were derived from the remaining 15 samples. The
ability of these classifiers to correctly predict the identity
of the left out sample was compared to the expected rate
due to chance alone (25% for 4 classes). Using the gene
expression classifiers derived from linear discriminant
analysis and 1-nearest neighbor methods, the arrays were
correctly classified 75% of the time. Classifiers from 3-
nearest neighbors and nearest centroid correctly predicted
the identity of the left-out array with 69% accuracy. To
assess the significance of the LOOCV results, Monte Carlo
simulation with 2,000 random permutations of the data-
set was also performed. The significance of the LOOCV
results was p < 0.001 for all prediction methods. Charac-
terized KEGG pathways were populated by the resulting
list of probesets significant at the p < 0.05 level (6066)
using Pathway Express, available at http://vor[36-39]. The p values for
each pathway were calculated using hypergeometric over
representation approach with Bonferroni's Multiple Com-
parisons Test.


Cell cycling analysis of infected HIGK
P. gingivalis were labeled overnight using live dye Cell-
Tracker Green BODIPY (Molecular Probes). S. gordonii
were labeled with live dye CellTracker Blue CMAC (Invit-
rogen) in a similar manner. HIGK were infected with the
same MOI used in the microarray experiments in the pres-
ence of 1 mM BrdU for four hours prior to treatment with
antibiotic/antimycotic and gentamicin (300 jig mL-1) to
kill extracellular bacteria. Cells were then allowed to grow
an additional twenty hours before harvesting for FACS
analysis. For quantitative analysis, similar infection con-
ditions were used but only P. gingivalis was labeled with
CellTracker Green BODIPY.

APC BrdU Flow kits were purchased from BD Pharmingen
(San Diego, CA) and used for flow cytometric analysis of
cell cycle. Briefly, bromodeoxyuridine (BrdU)-pulsed cells
were fixed and permeabilized with BD cytofix/cytoperm
buffers, after which DNAse was used to expose incorpo-
rated BrdU. APC conjugated anti-BrdU was used to label
newly incorporated BrdU while 7-amino-actinomycin D
(7-AAD) was used to stain total DNA content. Data was
acquired using a FACS Calibur (Becton Dickinson, Moun-
tain View, CA) and analyzed using FCS express software
(De Novo). Three independent experiments were per-
formed of each condition in triplicate. Statistical analyses
were performed using an ANOVA with Bonferroni's Mul-
tiple Comparison test.

HIGK growth analysis
Approximately 105 HIGK (ca. 10% confluence) were
seeded to T75 flasks in K-SFM with supplements. The cells
were co-cultured with single and complex mixtures of bac-
teria at 37C in 5% CO2 for 2 h at various MOI and under
the conditions described above. After infection, the cells
were washed three times with PBS and further cultured for
up to 144 hours in K-SFM supplemented with antibiotic/
antimycotic (Gibco/Invitrogen) and gentamicin (300 jig
mL-1). At each time-point, the cells were dissociated using
Accutase (Innovative Cell Technologies, San Diego, CA)
following the manufacturer's recommendations and cell
counts were determined using a Z1 Coulter Particle Coun-
ter (Beckman/Coulter). All cell counts were performed in
triplicate and all experiments were repeated twice.
ANOVA with Dunnett's Multiple Comparison Test was
used to determine statistical significance of each infection
condition compared to uninfected controls.

List of Abbreviations
(7-AAD): 7-amino-actinomycin D; (BrdU): bromodeoxy-
uridine, (CFU): colony forming units; (cDNA): comple-
mentary DNA; (cRNA): complementary RNA; (CDK):
cyclin-dependent kinase; (DNA): deoxyribonucleic acid;
(FACS): fluorescence-activated cell sorting; (GO, Gz, G):

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BMC Genomics 2009, 10:380

gap/growth phase; (GEC): gingival epithelial cells;
(HIGK): human immortalized gingival keratinocytes;
(HPV): human papillomavirus; (IL-8): interleukin 8; (K-
SFM): keratinocyte serum free media; (LOOCV): leave-
one-out cross-validation; (LAP): Localized Aggressive Per-
iodontitis; (M): mitosis; (MOI): multiplicity of infection;
(PBS): phosphate-buffered saline; (RNA): ribonucleic
acid; (S): synthesis phase.

Authors' contributions
JJM and KvL contributed equally to this work and should
be considered co-first authors. JJM participated in the
design of the study, aided the microbial-host cell co-cul-
ture studies, aided with all microarray experiments and
analysis, performed the supplemental HIGK growth anal-
yses experiments, and helped draft the manuscript. KvL
participated in the design of the study, carried out the
microbial-host cell co-culture studies, the cell cycling
analysis and drafted the manuscript. CD conducted con-
focal laser fluorescence microscopy experiments (data not
shown). SW aided in cell cycling analysis. SMW partici-
pated in design of the cell cycling analyses, performed the
statistical analysis, and helped to draft the manuscript.
HVB helped coordinate the microarray data analysis. RJL
participated in the design of the study and helped to draft
the manuscript. MH conceived the study, participated in
its design and coordination, performed the microarray
statistical analysis, conducted the HIGK growth analyses
experiments, and helped to draft the manuscript. All
authors read and approved the final manuscript.

Additional material

Additional file 1
"Antagonism of P. gingivalis-induced HIGK proliferation by S. gor-
donii is not due to indirect effects upon culture media." line graph
showing HIGK cell growth over time under 6 experimental conditions.
Click here for file

Additional file 2
"Antagonism of P. gingivalis-induced HIGK proliferation by S. gor-
donii is not due to indirect effects upon culture media." word docu-
ment describing the experimental conditions used to generate file 1.
Click here for file

Additional file 3
"Supplementary Materials and Methods. Antagonism of P. gingiva-
lis-induced HIGK proliferation by S. gordonii is not due to indirect
effects upon culture media." word document describing the materials
and methods used to generate file 1/supplementary figure.
Click here for file


This work was supported by UFCD Summer Research Fellowship (K.v.L.,
C.D. and S.W.), and NIH/NIDCR T32 Grant DE07200 (J.J.M.), RO I
DEI 6715 (M.H.) and ROI DEI I I I I (R.J.L). Microarray supervised analyses
were performed using BRB ArrayTools developed by Dr. Richard Simon
and BRB-ArrayTools Development Team. The authors would like to thank
Steve McClellan from the University of Florida ICBR for assistance with
cytometric analysis, and Jennifer VanPuymbrouck for assistance with micro-
biological assays on invasion and adhesion to HIGK.

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