Open Access

Subgingival bacterial colonization profiles correlate with gingival tissue gene expression

  • Panos N Papapanou1Email author,
  • Jan H Behle1,
  • Moritz Kebschull1,
  • Romanita Celenti1,
  • Dana L Wolf1,
  • Martin Handfield3,
  • Paul Pavlidis4 and
  • Ryan T Demmer2
BMC Microbiology20099:221

DOI: 10.1186/1471-2180-9-221

Received: 22 May 2009

Accepted: 18 October 2009

Published: 18 October 2009

Abstract

Background

Periodontitis is a chronic inflammatory disease caused by the microbiota of the periodontal pocket. We investigated the association between subgingival bacterial profiles and gene expression patterns in gingival tissues of patients with periodontitis. A total of 120 patients undergoing periodontal surgery contributed with a minimum of two interproximal gingival papillae (range 2-4) from a maxillary posterior region. Prior to tissue harvesting, subgingival plaque samples were collected from the mesial and distal aspects of each tissue sample. Gingival tissue RNA was extracted, reverse-transcribed, labeled, and hybridized with whole-genome microarrays (310 in total). Plaque samples were analyzed using checkerboard DNA-DNA hybridizations with respect to 11 bacterial species. Random effects linear regression models considered bacterial levels as exposure and expression profiles as outcome variables. Gene Ontology analyses summarized the expression patterns into biologically relevant categories.

Results

Wide inter-species variation was noted in the number of differentially expressed gingival tissue genes according to subgingival bacterial levels: Using a Bonferroni correction (p < 9.15 × 10-7), 9,392 probe sets were differentially associated with levels of Tannerella forsythia, 8,537 with Porphyromonas gingivalis, 6,460 with Aggregatibacter actinomycetemcomitans, 506 with Eikenella corrodens and only 8 with Actinomyces naeslundii. Cluster analysis identified commonalities and differences among tissue gene expression patterns differentially regulated according to bacterial levels.

Conclusion

Our findings suggest that the microbial content of the periodontal pocket is a determinant of gene expression in the gingival tissues and provide new insights into the differential ability of periodontal species to elicit a local host response.

Background

It is well established that the microbiota of the dental plaque are the primary etiologic agents of periodontal disease in humans [1]. The complex consortium of bacteria in the subgingival plaque biofilm [2] is in a state of dynamic equilibrium with the inflammatory response mounted in the adjacent gingival tissues, resulting in interdependent shifts in the composition of both the bacterial community and the ensuing inflammatory infiltrate. Indeed, it is known that microbial profiles of plaques harvested from healthy gingival sulci differ from those stemming from gingivitis or periodontitis lesions [3, 4]. Similarly, the cellular and molecular fabric of healthy gingival tissues differs from that of an incipient, early or established periodontitis lesion [5, 6].

A relatively new genomic tool that facilitates the study of the biology of cells, tissues or diseases is gene expression profiling, i.e., the systematic cataloging of messenger RNA sequences, and has provided enormous insights in the pathobiology of complex diseases, particularly in cancer research [7, 8]. Our group was the first to describe gingival tissue transcriptomes in chronic and aggressive periodontitis [9] and recently provided a comprehensive description of differential gene expression signatures in clinically healthy and diseased gingival units in periodontitis patients [10]. To date, a limited amount of data are available characterizing oral tissue transcriptomes in response to bacterial stimuli. In vitro experiments [1115] demonstrated a degree of specificity in the transcriptional responses of epithelial cells challenged by commensal or pathogenic species and have provided a foundation upon which in vivo studies can further contribute [16]. A pilot study including conventionally reared, germ free and SCID mice demonstrated that commensal microbial colonization influences the expression of innate host defense mediators at both the mRNA and the protein level in the periodontal tissues [17]. In a non-oral setting, a number of studies have examined the transcriptional profiles in response to microbial stimuli in intestinal [1822], gastric [23] and corneal epithelia [24].

In this publication, we expand our earlier work and investigate the association between the subgingival bacterial profile of the periodontal pocket and the whole genome transcriptome of the gingival tissue that is in intimate contact with the microbial biofilm.

Methods

The study design was approved by the Institutional Review Board of the Columbia University Medical Center.

Subjects

120 subjects with moderate to severe periodontitis [65 (54.2%) with chronic and 55 with aggressive periodontitis] were recruited among those referred to the Post-doctoral Periodontics Clinic of the Columbia University College of Dental Medicine. Eligible patients were (i) >13 yrs old; (ii) had ≥24 teeth; (iii) had no history of systematic periodontal therapy other than occasional prophylaxis, (iv) had received no systemic antibiotics or anti-inflammatory drugs for ≥6 months, (v) harbored ≥4 teeth with radiographic bone loss, (vi) did not have diabetes or any systemic condition that entails a diagnosis of "Periodontitis as a manifestation of systemic diseases" [25], (vii) were not pregnant, and (ix) were not current users of tobacco products or nicotine replacement medication. Signed informed consent was obtained prior to enrollment.

Clinical examination

All participants underwent a full-mouth examination of the periodontal tissues at six sites per tooth by a single, calibrated examiner. Variables recorded included presence/absence of visible dental plaque (PL), presence/absence of bleeding on probing (BoP), probing depth (PD), and attachment level (AL). Data were entered chair-side to a computer and stored at a central server.

Gingival tissue donor areas and tissue sample collection

Subsequently to clinical data entry, a specially developed software identified periodontally 'diseased' and 'healthy' tooth sites based on the clinical data. 'Diseased' sites showed BoP, had interproximal PD ≥4 mm, and concomitant AL ≥3 mm. 'Healthy' sites showed no BoP, had PD ≤4 mm and AL ≤2 mm. Next, the software identified (i) maxillary 'diseased' and 'healthy' interdental papillae, based on the above criteria, and (ii) pairs of diseased interdental papillae with similar clinical presentation (PD and AL within 2 mm of each other). A posterior maxillary sextant encompassing a pair of qualifying 'diseased' interdental papillae was identified.

Periodontal surgery was performed at the identified sextant with no prior supra- or subgingival instrumentation. After local anesthesia, submarginal incisions were performed, mucoperiosteal flaps were reflected, and the portion of each interproximal gingival papilla that adhered to the root surface was carefully dissected. This section comprised the epithelial lining of the interproximal periodontal pockets and the underlying connective tissue. After dissection, the gingival tissue specimens were thoroughly rinsed with sterile normal saline solution and transferred into Eppendorf tubes containing a liquid RNA stabilization reagent (RNAlater, Ambion, Austin, TX). A minimum of 2 diseased papillae were harvested from each sextant and, whenever available, a healthy tissue specimen was obtained from an adjacent site. After collection of the specimens, pocket elimination/reduction periodontal surgery was completed according to standard procedures. All patients received additional periodontal therapy according to their individual needs.

RNA extraction, reverse transcription, in vitro cRNA synthesis

The tissue specimens were stored in a liquid RNA stabilization reagent (RNAlater) overnight at 4°C, snap-frozen and stored in liquid nitrogen. All further processing occurred simultaneously for gingival biopsies originating from the same donor. Specimens were homogenized in Trizol (Invitrogen Life Technologies, Carlsbad, CA, USA). After incubation with chloroform and centrifugation at 12,000 g, RNA collected in the upper aqueous phase was precipitated by mixing with 75% isopropyl-alcohol and additional centrifugation and washings. The extracted RNA was purified using a total RNA isolation kit (RNeasy; Qiagen, Valencia, CA, USA), quantified spectrophotometrically, and 7.5 micrograms of total RNA were reverse-transcribed using a one-cycle cDNA synthesis kit (GeneChip Expression 3' amplification one-cycle cDNA synthesis kit; Affymetrix, Santa Clara, CA, USA). Synthesis of biotin-Labeled cRNA was performed using appropriate amplification reagents for in vitro transcription (GeneChip Expression 3'-Amplification Reagents for IVT labeling kit; Affymetrix). The cRNA yield was determined spectrophotometrically at 260 nm. Twenty μg of cRNA were fragmented by incubation in fragmentation buffer at 94°C for 35 min and stored at -80°C until hybridizations.

Gene Chip hybridizations

Whole genome microarrays (Human Genome U-133 Plus 2.0 arrays; Affymetrix) arrays, comprising 54,675 probe sets to analyze more than 47,000 transcripts including 38,500 well-characterized human genes, were used. Hybridizations, probe array scanning and gene expression analysis were performed at the Gene Chip Core Facility, Columbia University Genome Center. Each sample was hybridized once and each person contributed with 2 to 4 (median 3) tissue samples.

Harvesting of bacterial plaque

After identification of the interproximal papillae to be harvested and prior to periodontal surgery, subgingival plaque samples were obtained from the mesial and distal aspects of each gingival tissue sample using sterile curettes. After careful removal of supra-gingival plaque, the curette was placed subgingivally until the bottom of the probeable pocket was reached and subgingival plaque was collected by a single scaling stroke. The individual plaque samples were transferred into Eppendorf tubes containing 200 μl of sterile T-E buffer (10 mM Tris HCl, 1.0 mM EDTA, pH 7.6) and were not pooled at any stage of the processing described below.

Processing of plaque samples

Immediately after transfer to the laboratory the plaque pellet was re-suspended, vigorously vortexed, and 200 μl of a 0.5 M NaOH solution were added. Digoxigenin-labeled, whole genomic probes were prepared by random priming by the use of the High-Prime labeling kit (Roche/Boehringer-Mannheim, Indianapolis, IN, USA) from the following microbial strains: Aggregatibacter actinomycetemcomitans (ATCC 43718), Porphyromonas gingivalis (ATCC 33277), Tannerella forsythia (ATCC 43037), Treponema denticola (ATCC 35404), Prevotella intermedia (ATCC 25611), Fusobacterium nucleatum (ATCC 10953), Parvimonas micra (ATCC 33270), Campylobacter rectus (ATCC 33238), Eikenella corrodens (ATCC 23834), Veillonella parvula (ATCC 10790), and Actinomyces naeslundii (ATCC 49340). Further processing was carried out according to the checkerboard DNA-DNA hybridization method [26] as earlier described [27] with the following modifications: The chemiluminescent substrate used for detection was CSPD (Roche/Boehringer-Mannheim). Evaluation of the chemiluminescence signal was performed in a LumiImager F1 Workstation (Roche/Boehringer-Mannheim) by comparing the obtained signals with the ones generated by pooled standard samples containing 106 or 105 of each of the species. Standard curves were generated for each species by means of the LumiAnalyst software (Roche/Boehringer-Mannheim), and the obtained chemiluminescent signals were ultimately transformed into bacterial counts and exported into Excel files.

Statistical Analysis

In all analyses, either R version 2.3.1 (Linux OS) or SAS for PC version 9.1 (SAS Institute, Cary, NC) were used. Gene expression data were normalized and summarized using the log scale robust multi-array analysis (RMA, [28]) with default settings. Laboratory analysis provided a relative quantity of individual bacterial species for each plaque sample by comparison to known standards. Because the distribution of absolute bacterial counts was skewed, values were natural logarithm (ln) transformed, averaged within mouth and standardized by dividing each respective ln(bacterial count) by the population standard deviation for the respective species: one standard deviation on the ln scale (SDln) was treated as equivalent across microbes as previously described [29]. In addition to standardized scores for each individual microbe, we also defined three bacterial groupings ('etiologic burden' (EB), 'putative burden' (PB), and 'health-associated burden (HAB), by summing the standardized values for the various subsets of species as follows [29]: To define EB, we utilized (i) the consensus report of the 1996 World Workshop in Periodontics identifying three bacterial species (P. gingivalis, T. forsythia and A. actinomycetemcomitans) as causally related to periodontitis [30], and (ii) Socransky's "Red Complex" [31] further identifying T. denticola as a species that closely co-varies with P. gingivalis and T. forsythia in pathological periodontal pockets. The 5 bacterial species deemed putatively associated with periodontal disease (C. rectus, E. corrodens, F. nucleatum, P. micra and P. intermedia) were grouped as PB [30]. Finally, HAB included two 'health-associated' bacterial species, A. naeslundii and V. parvula[31].

Differential gene expression was the dependent variable in standard mixed-effects linear regression models which considered patient effects as random with a normal distribution. Standardized bacterial count and gingival tissue status ('healthy' vs. 'diseased') were modeled as fixed effects. Bacterial count was defined as the average value derived from two plaque samples collected from the mesial and distal sites flanking each of harvested papilla, respectively. Gingival tissue status was included in the model to adjust for the confounding effects related to unmeasured characteristics of disease vs. healthy tissue (e.g., tissue properties affecting bacterial colonization or levels of non-investigated bacterial species). To further minimize the potential for confounding, we conducted alternate analyses restricted to diseased tissue and further adjusted for probing depth. Statistical significance for each probe set was determined using both the Bonferroni criterion and q-value [32]. For each probe set, a fold-change was computed by taking the following ratio: raw expression values among gingival tissue samples adjacent to periodontal sites with fifth quintile bacterial colonization levels vs. expression values in samples adjacent to first quintile colonization levels. Therefore, fold-change values represent relative RNA levels in tissues adjacent to 'high' vs. 'low' bacterial colonization sites.

Gene Ontology analysis was performed using ermineJ [33] with the Gene Score Resampling method. P-values generated from the aforementioned mixed-models, were used as input to identify biologically-relevant groups of genes showing differential expression in relation to bacterial colonization. Gene symbols and descriptions were derived from the Gemma System (HG-U133_Plus_2_NoParents.an.zip) and downloaded from http://chibi.ubc.ca/microannots/. Experimental details and results following the MIAME standards [34] are available at the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE16134.

Real-time RT-PCR Confirmations

To independently confirm the expression data generated by the microarray experiments, we performed quantitative real-time RT-PCR analyses for three genes strongly differentially regulated by subgingival bacterial levels (Sperm-associated antigen4 (Spag4), POU class 2 associating factor 1 (POU2AF1), and SLAM family member 7 (SlamF7), while glyceraldehyd-3-phosphatedehydrogenase (GAPDH) was used as a constitutively expressed control gene. In this confirmatory step we used subset of five patients, selected upon the basis of strong differential regulation of the above genes, each contributing with both a 'healthy' and a 'diseased' tissue sample.

In brief, quantitative real-time PCR was performed as described previously [35]. The Taqman Gene Expression Assays Hs00162127_m1, Hs00221793_m1, Hs01573371_m1, and Hs99999905_m1 were used for Spag4, POU2AF1, SlamF7, and glyceraldehyd-3-phosphatedehydrogenase (GAPDH), respectively (Applied Biosystems, Foster City, CA). Three technical replicates per sample and gene were performed. Since in the Affymetrix microarray platform, genes are often represented by multiple oligonucleotide probes, we calculated means of the normalized expression data for all probes mapping to the three genes in each gingival tissue specimen. Subsequently, we calculated Spearman correlation coefficients for the mean microarray expression values and the Δct values obtained by quantitative RT-PCR.

Results

The mean age of the enrolled patients was 39.9 years (range 13-76). Sixty one patients (50.8%) were male. Based on self-reported race/ethnicity, 39.2% of the participants were White, 21.7% Black, 27.5% of mixed race, and 73.3% Hispanic.

Among the 310 harvested gingival tissue samples, 69 originated from periodontally healthy sites and 241 from periodontally diseased sites. No healthy tissue samples were available from 51 patients. Probing pocket depth values ranged from 1 to 4 mm in the healthy tissue samples, and between 5 and 11 mm in the diseased tissue samples.

Table 1 describes the subgingival bacterial load in the periodontal pockets adjacent to the 310 harvested gingival tissue samples. As indicated by the > 0 minimum values for all bacteria, all tissue samples were in contact with biofilms that were ubiquitously inhabited by all 11 investigated species. However, the subgingival colonization level by each species varied greatly. Median levels of P. intermedia, T. forsythia, and F. nucleatum were highest while levels of A. actinomycetemcomitans, C. rectus and V. parvula were lowest in the pockets adjacent to the obtained gingival tissue specimens.
Table 1

Subgingival bacterial load a in the periodontal pockets adjacent to the harvested gingival tissue samples

Bacterial species

Minimum

25th pctl b

Median

75th pctl

Maximum

AVG

SD

A. actinomycetemcomitans

1.4

8.9

18.1

63.6

2306.9

58.5

154.5

P. gingivalis

1.1

8.8

112.6

442.0

9740.5

379.3

821.5

T. forsythia

3.1

44.4

236.2

758.0

4867.5

543.2

762.9

T. denticola

1.6

12.2

70.2

201.3

3318.5

190.8

360.5

P. intermedia

4.0

88.2

245.4

531.8

7189.3

470.2

762.7

F. nucleatum

14.4

113.1

200.4

348.6

6470.1

270.4

399.5

P. micra

4.6

60.7

118.3

211.4

5606.5

189.1

351.9

C. rectus

1.1

10.3

28.3

76.3

2457.8

89.1

219.1

E. corrodens

1.0

14.3

29.0

71.8

2801.0

74.9

185.6

V. parvula

1.5

17.1

35.8

95.2

3004.0

105.1

238.9

A. naeslundii

3.8

93.5

179.1

408.3

11353.1

434.4

1003.2

a Values are bacterial counts × 10 000, obtained through checkerboard DNA-DNA hybridization, and represent the average load of the two pockets adjacent to each tissue sample.

b Percentile.

Regression models adjusted for clinical status (periodontal health or disease) were used to identify probe sets whose differential expression in the gingival tissues varied according to the subgingival level of each of the 11 investigated species. Using a p-value of < 9.15 × 10-7 (i.e., using a Bonferroni correction for 54,675 comparisons), the number of differentially expressed probe sets in the gingival tissues according to the level of subgingival bacterial colonization was 6,460 for A. actinomycetemonitans; 8,537 for P. gingivalis; 9,392 for T. forsythia; 8,035 for T. denticola; 7,764 for P. intermedia; 4,073 for F. nucleatum; 5,286 for P. micra; 9,206 for C. rectus; 506 for E. corrodens; 3,550 for V. parvula; and 8 for A. naeslundii. Table 2 presents the top 20 differentially expressed probe sets among tissue samples with highest and lowest levels of colonization (i.e., the upper and the lower quintiles) by A. actinomycetemcomitans, P. gingivalis and C. rectus, respectively, sorted according to decreasing levels of absolute fold change. Additional Files 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 present all the statistically significantly differentially expressed genes for each of the 11 species. Overall, levels of bacteria known to co-vary in the subgingival environment, such as those of the "red complex" [31]) species (P. gingivalis, T. forsythia, and T. denticola) were found to be associated with similar gene expression signatures in the gingival tissues. Absolute fold changes in gene expression were sizeable among the top 50 probes sets for these three species (range 11.2-5.5 for P. gingivalis, 10.4-5.3 for T. forsythia, and 8.9-5.0 for T. denticola). Corresponding fold changes for the top differentially expressed probe sets ranged between 9.0 and 4.7 for C. rectus, 6.9-3.8 for P. intermedia, 6.8-4.1 for P. micra, 5.8-2.2 for A. actinomycetemcomitans, 4.6-2.9 for V. parvula, 4.3-2.8 for F. nucleatum, 3.2-1.8 for E. corrodens, and 2.0-1.5 for A. naeslundii. Results for the 'etiologic', 'putative' and 'health-associated' bacterial burdens were consistent with the those for the individual species included in the respective burden scores, and the top 100 probe sets associated with each burden are presented in Additional Files 12, 13, 14.
Table 2

Top 20 differentially regulated genes in gingival tissues according to subgingival levels of A. actinomycetemcomitans, P. gingivalis and C. rectus.

Rank

A. actinomycetemcomitans

 

P. gingivalis

 

C. rectus

 
 

Gene a

FC b

Gene

FC

Gene

FC

1

hypothetical protein MGC29506

5.76

hypothetical protein MGC29506

11.21

hypothetical protein MGC29506

9.04

2

tumor necrosis factor receptor superfamily, member 17

4.23

non-annotated

8.64

non-annotated

7.62

3

sperm associated antigen 4

4.01

tumor necrosis factor receptor superfamily, member 17

7.92

tumor necrosis factor receptor superfamily, member 17

6.48

4

interferon, alpha-inducible protein 6

3.91

immunoglobulin kappa variable 1-5

7.59

POU domain, class 2, associating factor 1

6.37

5

POU domain, class 2, associating factor 1

3.86

non-annotated

7.51

immunoglobulin heavy variable 1-69

6.34

6

CD79a molecule, immunoglobulin-associated alpha

3.65

immunoglobulin kappa variable 1-5

7.42

sperm associated antigen 4

6.14

7

FK506 binding protein 11, 19 kDa

3.58

immunoglobulin heavy variable 1-69

7.41

KIAA0125

6.10

8

hypothetical protein MGC29506

3.56

interferon, alpha-inducible protein 6

7.38

interferon, alpha-inducible protein 6

5.93

9

immunoglobulin lambda locus, immunoglobulin lambda constant 1

3.50

POU domain, class 2, associating factor 1

7.18

immunoglobulin kappa constant, immunoglobulin kappa variable 1-5

5.92

10

immunoglobulin heavy constant alpha 1

3.47

immunoglobulin kappa variable 1-5

7.16

interferon, alpha-inducible protein 6

5.72

11

KIAA0746 protein

3.41

interferon, alpha-inducible protein 6

6.97

immunoglobulin heavy constant alpha 1

5.65

12

CD79a molecule, immunoglobulin-associated alpha

3.39

non-annotated

6.96

Fc receptor-like 5

5.60

13

family with sequence similarity 46, member C

3.34

immunoglobulin heavy constant alpha 1

6.89

non-annotated

5.55

14

non-annotated

3.34

interferon, alpha-inducible protein 6

6.87

interferon, alpha-inducible protein 6

5.53

15

interferon, alpha-inducible protein 6

3.26

Fc receptor-like 5

6.85

interferon, alpha-inducible protein 6

5.52

16

potassium intermediate/small conductance calcium-activated channel, subfamily N, member 3

3.20

KIAA0125

6.79

immunoglobulin lambda locus, immunoglobulin lambda constant 1 (Mcg marker)

5.49

17

immunoglobulin lambda locus, immunoglobulin lambda constant 1 (Mcg marker)

3.16

immunoglobulin kappa variable 1-5

6.70

interferon, alpha-inducible protein 6, immunoglobulin heavy locus (G1m marker)

5.39

18

KIAA0746 protein

3.12

immunoglobulin lambda locus

6.67

non-annotated

5.37

19

SLAM family member 7

3.11

immunoglobulin lambda locus, immunoglobulin lambda constant 1 (Mcg marker)

6.63

immunoglobulin lambda locus, immunoglobulin lambda constant 1 (Mcg marker)

5.36

20

interferon, alpha-inducible protein 6

3.03

sperm associated antigen 4

6.59

immunoglobulin kappa constant, immunoglobulin kappa variable 1-5

5.35

a Repeated occurrence of the same gene among the top ranked is due to multiple probe sets mapping to the same gene

b Fold change indicates ratio of expression in gingival tissues in the upper over the lower quintile of colonization by the particular species

Additional regression models utilized data from diseased gingival tissue samples only and included probing pocket depth as an additional continuous covariate. These analyses, despite attenuated p-values and fold changes, confirmed that colonization by specific bacteria remained significantly associated with a differential gene expression in the gingival tissues. For example, in this analysis, among the top 50 differentially expressed probes sets according to colonization levels by P. gingivalis, T. forsythia or T. denticola, the ranges of absolute fold changes were 2.8 - 4.5, 3.3 - 5.5 and 2.5 - 4.3, respectively. All of the top 50 probe sets for each species maintained an FDR<0.05.

Table 3 presents the Spearman correlation coefficients between microarray-generated expression data and Δct values (PCR cycles) of quantitative real-time RT-PCR for three selected genes SPAG4, POU2AF1 and SLAMF7. Since lower Δct values indicate higher levels of expression, the calculated highly negative correlation coefficients between microrray-based expression values and Δct values represent strong and significant positive correlation between data generated by the two platforms.
Table 3

Correlation between microarray-based expression data and real time RT-PCR Δct values (PCR cycles) for three genes.

Gene

Spearman correlation coefficient

p-value

Spag4 a

-0.95

0.0004

POU2AF1 b

-0.94

0.0011

SlamF7 c

-0.82

0.0058

a Sperm-associated antigen 4

b POU class 2 associating factor 1

c SLAM family member 7

Gene ontology (GO) analyses identified biological processes that appeared to be differentially regulated in the gingival tissues in relation to subgingival colonization. Additional File 15 provides a complete list of all the statistically significantly regulated GO groups for each of the 11 species. Table 4 exemplifies commonalities and differences in gingival tissue gene expression on the Gene Ontology level with respect to colonization levels by A. actinomycetemcomitans and the three "red complex" bacteria. The left column of the Table lists the 20 most strongly differentially regulated GO groups according to levels of A. actinomycetemcomitans, while the next three columns indicate the ranking of each particular GO group for P. gingivalis, T. forsythia and T. denticola, respectively. Although antigen processing and presentation was the highest ranked (i.e., most strongly differentially regulated) GO group for all four species, the second ranked GO group in the A. actinomycetemcomitans column (apoptotic mitochondrial changes) was ranked 96th, 101st and 96th, respectively, for the other three bacteria. Likewise, the fifth ranked group in the A. actinomycetemcomitans column (phosphate transport) was ranked 56th, 63rd and 71st, respectively for the three 3 "red complex" species. Protein-chromophore linkage (ranked 8th for A. actinomycetemcomitans) ranked between 147th and 152nd for the other three species. Conversely, second-ranked regulation of cell differentiation for the "red complex" species, ranked 19th for A. actinomycetemcomitans.
Table 4

Patterns of gene expression in gingival tissues, according to subgingival levels of A. actinomycetemcomitans, P. gingivalis, T. forsythia and T. denticola.

 

A. actinomycetemcomitans

P. gingivalis

T. forsythia

T. denticola

1

antigen processing and presentation

1

1

1

2

apoptotic mitochondrial changes

96

101

96

3

antigen processing and presentation of peptide antigen

3

3

3

4

antigen processing and presentation of peptide antigen via MHC class I

4

3

5

5

phosphate transport

56

63

71

6

muscle development

38

39

44

7

MAPKKK cascade

5

4

7

8

protein-chromophore linkage

152

150

147

9

hemopoietic or lymphoid organ development

9

11

10

10

hemopoiesis

11

12

11

11

immune system development

8

10

9

12

protein amino acid N-linked glycosylation

50

81

52

13

fatty acid biosynthetic process

17

21

8

14

regulation of anatomical structure morphogenesis

7

6

7

15

acute inflammatory response

24

18

21

16

humoral immune response

37

40

35

17

activation of immune response

62

58

54

18

regulation of cell adhesion

51

45

47

19

regulation of cell differentiation

2

2

2

20

hemostasis

12

15

14

The left column lists the top 20 differentially expressed Gene Ontology (GO) groups, according to levels of A. actinomycetemcomitans while columns to the right describe the ranking of these particular GO groups for the other three species.

Figure 1 provides a visual illustration of a cluster analysis that further underscores the level of similarity in gingival tissue gene expression according to colonization by each of the 11 investigated species. The clusters identify bacterial species whose subgingival colonization levels are associated with similar patterns of gene expression in the adjacent gingival tissues. The relative proximity of the investigated species on the x-axis reflects the similarity among the corresponding gingival gene expression signatures. The color of the heat map indicates the relative strength of differential regulation of each particular GO group (i.e., each pixel row) among the 11 species, with yellow/white colors indicating strong regulation and red colors a weaker regulation. Not unexpectedly, "red complex" bacteria clustered closely together, but were interestingly far apart from A. actinomycetemcomitans, which showed higher similarity with E. corrodens and A. naeslundii.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2180-9-221/MediaObjects/12866_2009_Article_892_Fig1_HTML.jpg
Figure 1

Cluster analysis of Gene Ontology (GO) groups differentially expressed in gingival tissues according to subgingival colonization by the 11 investigated species. The clusters identify bacterial species whose subgingival colonization levels are associated with similar patterns of gene expression in the adjacent gingival tissues. The color of the heat map indicates the relative strength of differential regulation of each particular GO group (i.e., each pixel row) among the 11 investigated species, with yellow/white colors indicating strong regulation and red colors weaker regulation.

Discussion

To the best of our knowledge, this is the first study to examine the association between subgingival bacterial colonization patterns and gingival tissue gene expression in human periodontitis. Our data demonstrate that the variable bacterial content of the periodontal pocket correlates with distinct gene expression signatures in the adjacent gingival tissues. Importantly, even though we examined colonization patterns by only a limited number of bacterial species, we found that the variable subgingival bacterial load by several -but clearly not all- species correlated significantly with tissue gene expression. In other words, and to paraphrase both Anton van Leeuwenhoek and George Orwell, our data indicate that all subgingival "animalcules" are not "equal" in this respect.

In a recent publication [10], we presented transcriptomic data from a subset of patients involved in the present report (90 patients and 247 arrays out of the total of 120 patients and 310 arrays included here) and compared gene expression profiles of clinically healthy and diseased gingival tissues in patients with periodontitis. We documented substantial differential gene expression between states of gingival health and disease that was reflected both by genes that were a priori anticipated to be variably expressed based on current knowledge (e.g., several inflammatory, immune function- and apoptosis-related genes), but also by genes that are not readily associated with gingival inflammation (e.g., the transcription factor POU2AF1, the sperm associated antigen 4 which appears to be associated with apoptosis (own unpublished data), the cell adhesion-mediating protein desmocollin 1, and the signaling lymphocytic activation molecule family member 7). In the present study, we sought to investigate whether the bacterial content of the periodontal pocket is also a determinant of gene expression in the adjacent gingival tissues in order to enhance our understanding of the host-bacterial interactions that take place in the interface between the plaque biofilm and the periodontal pocket. We realize that the above question can ideally be addressed in a longitudinal prospective rather than a cross-sectional study. Thus, although our analyses considered bacterial colonization as the independent exposure and tissue gene expression as the outcome, it is impossible to rule out reverse causation, i.e., that the qualitative characteristics of the gingival tissue are the determinants of bacterial colonization. However, given that periodontitis is a bacterially-induced infection, the former approach is reasonable in the discussion of the observed correlations between colonization patterns and tissue gene expression signatures. We also want to draw the reader's attention to the fact that, despite our inferences on each particular bacterial species' effect on the gingival tissue transcriptome, we have not studied individual mono-infections. Therefore, any properties ascribed to a particular species with respect to its ability to regulate genes in the gingival tissues cannot be entirely segregated from concomitant synergistic or antagonistic effects of other covarying bacteria among the ones studied or, most importantly, of the several hundreds of cultivable and uncultivable species that are known to colonize the periodontal pocket and were not investigated in this work [36]. Instead, the differential gene expression in the gingival tissues should more appropriately be attributed to the aggregate effect of the mixed microbial burden, and the specific investigated bacteria may simply serve as a surrogate for this mixed microbial burden to which they contribute. It must be further recognized that the gingival tissue transcriptomes are also influenced by a plethora of additional factors beyond those of bacterial origin, including biologically active host-derived molecules and tissue degradation byproducts, that could not be accounted for in our study.

In view of the above, and because the transcriptomic profiles analyzed originate from a mixed cell population comprising gingival epithelial cells, connective tissue fibroblasts and infiltrating cells, our data are not directly comparable with observations from the aforementioned in vitro studies of mono-infections of oral epithelial cell lines. Nevertheless, our data corroborate and extent data from these experimental settings. For example, ontology analysis of epithelial cell pathways differentially regulated after infection with F. nucleatum[14] identified MAPK signaling and regulation of actin cytoskeleton among the impacted pathways. Likewise, in line with observations by Handfield et al. [11], apoptotic mitochondrial changes, the second highest differentially regulated ontology group according to levels of A. actinomycetemcomitans was ranked 96th according to subgingival levels of P. gingivalis. Indeed, A. actinomycetemcomitans is known to exert strong pro-apoptotic effects on various cell types encountered in inflamed gingival tissues, such as gingival epithelial cells [37] or invading mononuclear cells [38], attributed in part to its potent cytolethal distending toxin [39]. On the other hand, P. gingivalis was shown to inhibit apoptosis in primary gingival epithelial cells by ATP scavenging through its ATP-consuming nucleoside diphosphate kinase [40]. In contrast, other in vitro studies involving oral epithelial cells (for review see [41]) reported apoptotic cell death induced by P. gingivalis at very high (up to 1:50,000) multiplicities of infection [42], which arguably exceeds the in vivo burden in the periodontal pocket.

Thus, our data indicate presence of pro-apoptotic alterations in the gingival tissues in A. actinomycetemcomitans-associated periodontitis, while the effects of P. gingivalis appear to be primarily mediated by other pathways. Interestingly, our data corroborate a recent study that explored the hyper-responsiveness of peripheral blood neutrophils in periodontitis and demonstrated a significantly increased expression of several interferon-stimulated genes [43]. As shown in Table 2, interferon alpha inducible protein-6 was among the top commonly up-regulated genes in gingival tissue lesions according to levels of A. actinomycetemcomitans, P. gingivalis and C. rectus, and tissue-infiltrating neutrophils are a conceivable source for these transcripts.

In general, the magnitude of the differential expression of host tissue genes according to levels of A. actinomycetemcomitams (with a total of 68 genes exceeding an absolute fold change of 2 when comparing tissue samples in the upper and lowest quintiles of subgingival colonization; Additional File 1) was more limited than that of bacteria in the 'red complex' (488 genes for P. gingivalis, 521 genes for T. forsythia, 429 genes for T. denticola; Additional Files 2, 3, 4) or C. rectus (450 genes; Additional File 8).

The null hypothesis underlying the present study, i.e., that variable subgingival bacterial load by specific bacteria results in no differential gene expression in the adjacent pocket tissues, was rejected by our data. Indeed levels of only 2 of the 11 species investigated appeared to correlate poorly with differential gene expression in the tissues: A. naeslundii, whose levels were statistically associated with differential expression of only 8 probe sets out of the approximately 55,000 analyzed, and E. corrodens with <1% of the probe sets being differentially regulated between pockets with the highest versus the lowest levels of colonization. In contrast, 15-17% of the examined probes sets were differentially expressed according to subgingival levels of the "red complex" species and C. rectus, whose levels were the most strongly correlated with gingival tissue gene expression signatures among all investigated species.

Importantly, the above associations between bacterial colonization and gingival tissue gene expression signatures were confirmed in analyses adjusting for clinical periodontal status, although they were expectedly attenuated. In other words, the difference in the tissue transcriptomes between periodontal pockets with high versus low levels of colonization by the particular species identified as strong regulators of gene expression cannot solely be ascribed to differences in the clinical status of the sampled tissues [10] which is known to correlate well with bacterial colonization patterns [31]. Instead, our analyses based on either statistical adjustment or restriction to 'diseased' tissue samples consistently demonstrate that, even among periodontal pockets with similar clinical characteristics, the subgingival colonization patterns still influence the transcriptome of the adjacent gingival tissues. Assuming a generally positive correlation between gene and protein expression [44, 45], this finding is conceptually important as it suggests that the 'phenotype' of the periodontal pocket, and by extension its potential to experience additional periodontal tissue breakdown and/or an unfavorable or favorable treatment response, is also dependent on its bacterial content, and not merely on the traditional clinical parameters (non-specific plaque accumulation, bleeding on probing, probing depth and attachment level). It also provides biology-founded ammunition in favor of the controversial argument that microbial diagnostics have a place in the decision-making and therapeutic management of patients with periodontitis [46].

Finally, we emphasize that the subject sample involved in the present study included both chronic and aggressive periodontitis patients and subjectsbelonging to various race/ethnicity groups. It is conceivable that the typeof disease and race/ethnicity-related charactersitics may be additional determinants of the gingival tissue transcriptome and/or may act asmodifiers of the association between bacterial colonization patterns andtissue gene expression. We intend to explore these possibilities insubsequent reports.

Conclusion

Using data from 120 patients, 310 gingival tissue samples and the adjacent 616 subgingival plaque samples, we demonstrate a strong correlation between the bacterial content of the periodontal pocket and the gene expression profile of the corresponding gingival tissue. The findings indicate that the subgingival bacterial load by several - but clearly not all - investigated periodontal species may determine gene expression in the adjacent gingival tissues. These cross-sectional observations may serve as a basis for future longitudinal prospective studies of the microbial etiology of periodontal diseases.

Declarations

Acknowledgements

This work was supported by grant DE015649 and a CTSA Award RR025158 (P.N.P.). Additional support was provided by K99 DE-018739 (R.T.D); GM076990, a Michael Smith Foundation for Health Research Career Investigator Award, and an Award from the Canadian Institutes of Health Research (P.P); DE16715 (M.H.); Neue Gruppe Wissenschaftsstiftung, Wangen/Allgäu, Germany and IADR/Philips Oral Healthcare Young Investigator Research Grant (M.K).

Authors’ Affiliations

(1)
Division of Periodontics, Section of Oral and Diagnostic Sciences, College of Dental Medicine, Columbia University
(2)
Department of Epidemiology, Mailman School of Public Health, Columbia University
(3)
Center for Molecular Microbiology and Department of Oral Biology, University of Florida, College of Dentistry
(4)
Department of Psychiatry and Center of High-Throughput Biology, Michael Smith Laboratories, University of British Columbia

References

  1. Socransky SS, Haffajee AD: Periodontal microbial ecology. Periodontol 2000. 2005, 38: 135-187. 10.1111/j.1600-0757.2005.00107.x.PubMedView ArticleGoogle Scholar
  2. Marsh PD: Dental plaque: biological significance of a biofilm and community lifestyle. J Clin Periodontol. 2005, 32 (Suppl 6): 7-15. 10.1111/j.1600-051X.2005.00790.x.PubMedView ArticleGoogle Scholar
  3. Listgarten MA, Helldén L: Relative distribution of bacteria at clinically healthy and periodontally diseased sites in humans. J Clin Periodontol. 1978, 5 (2): 115-132. 10.1111/j.1600-051X.1978.tb01913.x.PubMedView ArticleGoogle Scholar
  4. Socransky SS, Haffajee AD, Smith C, Dibart S: Relation of counts of microbial species to clinical status at the sampled site. J Clin Periodontol. 1991, 18 (10): 766-775. 10.1111/j.1600-051X.1991.tb00070.x.PubMedView ArticleGoogle Scholar
  5. Page RC, Schroeder HE: Pathogenesis of inflammatory periodontal disease. A summary of current work. Laboratory investigation; a journal of technical methods and pathology. 1976, 34 (3): 235-249.PubMedGoogle Scholar
  6. Offenbacher S: Periodontal diseases: pathogenesis. Ann Periodontol. 1996, 1 (1): 821-878.PubMedView ArticleGoogle Scholar
  7. Chung CH, Bernard PS, Perou CM: Molecular portraits and the family tree of cancer. Nat Genet. 2002, 32 (Suppl): 533-540. 10.1038/ng1038.PubMedView ArticleGoogle Scholar
  8. Quackenbush J: Microarray analysis and tumor classification. N Engl J Med. 2006, 354 (23): 2463-2472. 10.1056/NEJMra042342.PubMedView ArticleGoogle Scholar
  9. Papapanou PN, Abron A, Verbitsky M, Picolos D, Yang J, Qin J, Fine JB, Pavlidis P: Gene expression signatures in chronic and aggressive periodontitis: a pilot study. Eur J Oral Sci. 2004, 112 (3): 216-223. 10.1111/j.1600-0722.2004.00124.x.PubMedView ArticleGoogle Scholar
  10. Demmer RT, Behle JH, Wolf DL, Handfield M, Kebschull M, Celenti R, Pavlidis P, Papapanou PN: Transcriptomes in healthy and diseased gingival tissues. J Periodontol. 2008, 79 (11): 2112-2124. 10.1902/jop.2008.080139.PubMed CentralPubMedView ArticleGoogle Scholar
  11. Handfield M, Mans JJ, Zheng G, Lopez MC, Mao S, Progulske-Fox A, Narasimhan G, Baker HV, Lamont RJ: Distinct transcriptional profiles characterize oral epithelium-microbiota interactions. Cell Microbiol. 2005, 7 (6): 811-823. 10.1111/j.1462-5822.2005.00513.x.PubMedView ArticleGoogle Scholar
  12. Mans JJ, Lamont RJ, Handfield M: Microarray analysis of human epithelial cell responses to bacterial interaction. Infect Disord Drug Targets. 2006, 6 (3): 299-309. 10.2174/187152606778249926.PubMedView ArticleGoogle Scholar
  13. Mans JJ, Baker HV, Oda D, Lamont RJ, Handfield M: Distinctive characteristics of transcriptional profiles from two epithelial cell lines upon interaction with Actinobacillus actinomycetemcomitans. Oral Microbiol Immunol. 2006, 21 (4): 261-267. 10.1111/j.1399-302X.2006.00281.x.PubMedView ArticleGoogle Scholar
  14. Hasegawa Y, Mans JJ, Mao S, Lopez MC, Baker HV, Handfield M, Lamont RJ: Gingival epithelial cell transcriptional responses to commensal and opportunistic oral microbial species. Infect Immun. 2007, 75 (5): 2540-2547. 10.1128/IAI.01957-06.PubMed CentralPubMedView ArticleGoogle Scholar
  15. Milward MR, Chapple IL, Wright HJ, Millard JL, Matthews JB, Cooper PR: Differential activation of NF-kappaB and gene expression in oral epithelial cells by periodontal pathogens. Clin Exp Immunol. 2007, 148 (2): 307-324. 10.1111/j.1365-2249.2007.03342.x.PubMed CentralPubMedView ArticleGoogle Scholar
  16. Handfield M, Baker HV, Lamont RJ: Beyond good and evil in the oral cavity: insights into host-microbe relationships derived from transcriptional profiling of gingival cells. J Dent Res. 2008, 87 (3): 203-223. 10.1177/154405910808700302.PubMed CentralPubMedView ArticleGoogle Scholar
  17. Dixon DR, Reife RA, Cebra JJ, Darveau RP: Commensal bacteria influence innate status within gingival tissues: a pilot study. J Periodontol. 2004, 75 (11): 1486-1492. 10.1902/jop.2004.75.11.1486.PubMedView ArticleGoogle Scholar
  18. Hooper LV, Wong MH, Thelin A, Hansson L, Falk PG, Gordon JI: Molecular analysis of commensal host-microbial relationships in the intestine. Science. 2001, 291 (5505): 881-884. 10.1126/science.291.5505.881.PubMedView ArticleGoogle Scholar
  19. Rawls JF, Samuel BS, Gordon JI: Gnotobiotic zebrafish reveal evolutionarily conserved responses to the gut microbiota. Proc Natl Acad Sci USA. 2004, 101 (13): 4596-4601. 10.1073/pnas.0400706101.PubMed CentralPubMedView ArticleGoogle Scholar
  20. Sonnenburg JL, Chen CT, Gordon JI: Genomic and metabolic studieof the impact of probiotics on a model gut symbiont and host. PLoS biology. 2006, 4 (12): e413-10.1371/journal.pbio.0040413.PubMed CentralPubMedView ArticleGoogle Scholar
  21. Chowdhury SR, King DE, Willing BP, Band MR, Beever JE, Lane AB, Loor JJ, Marini JC, Rund LA, Schook LB, et al: Transcriptome profiling of the small intestinal epithelium in germfree versus conventional piglets. BMC Genomics. 2007, 8: 215-10.1186/1471-2164-8-215.PubMed CentralPubMedView ArticleGoogle Scholar
  22. Romond MB, Mullie C, Colavizza M, Revillion F, Peyrat JP, Izard D: Intestinal colonization with bifidobacteria affects the expression of galectins in extraintestinal organs. FEMS Immunol Med Microbiol. 2009, 55 (1): 85-92. 10.1111/j.1574-695X.2008.00501.x.PubMedView ArticleGoogle Scholar
  23. Brzozowski T, Zwolinska-Wcislo M, Konturek PC, Kwiecien S, Drozdowicz D, Konturek SJ, Stachura J, Budak A, Bogdal J, Pawlik WW, et al: Influence of gastric colonization with Candida albicans on ulcer healing in rats: effect of ranitidine, aspirin and probiotic therapy. Scandinavian journal of gastroenterology. 2005, 40 (3): 286-296. 10.1080/00365520510011524.PubMedView ArticleGoogle Scholar
  24. Xue ML, Thakur A, Lutze-Mann L, Willcox MD: Pro-inflammatory cytokine/chemokine gene expression in human corneal epithelial cells colonized by Pseudomonas aeruginosa. Clinical & experimental ophthalmology. 2000, 28 (3): 197-200. 10.1046/j.1442-9071.2000.00286.x.View ArticleGoogle Scholar
  25. Lindhe J, Ranney RR, Lamster IB, Charles A, Chung CH, Flemmig TF, Kinane DF, Listgarten MA, Löe H, Schoor R, et al: Consensus report: Periodontitis as a manifestation of systemic diseases. Ann Periodontol. 1999, 4 (1): 64-10.1902/annals.1999.4.1.64.View ArticleGoogle Scholar
  26. Socransky SS, Smith C, Martin L, Paster BJ, Dewhirst FE, Levin AE: "Checkerboard" DNA-DNA hybridization. Biotechniques. 1994, 17 (4): 788-792.PubMedGoogle Scholar
  27. Papapanou PN, Neiderud A-M, Papadimitriou A, Sandros J, Dahlén G: "Checkerboard" assessments of periodontal microbiota and serum antibody responses: A case-control study. J Periodontol. 2000, 71 (6): 885-897. 10.1902/jop.2000.71.6.885.PubMedView ArticleGoogle Scholar
  28. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics (Oxford, England). 2003, 4 (2): 249-264.View ArticleGoogle Scholar
  29. Desvarieux M, Demmer RT, Rundek T, Boden-Albala B, Jacobs DR, Sacco RL, Papapanou PN: Periodontal microbiota and carotid intima-media thickness: the Oral Infections and Vascular Disease Epidemiology Study (INVEST). Circulation. 2005, 111 (5): 576-582. 10.1161/01.CIR.0000154582.37101.15.PubMed CentralPubMedView ArticleGoogle Scholar
  30. World Workshop in Periodontics: Consensus report periodontal diseases: Pathogenesis and microbial factors. Annals of Periodontol. 1996, 1 (1): 926-932.View ArticleGoogle Scholar
  31. Socransky SS, Haffajee AD, Cugini MA, Smith C, Kent RL: Microbial complexes in subgingival plaque. J Clin Periodontol. 1998, 25 (2): 134-144. 10.1111/j.1600-051X.1998.tb02419.x.PubMedView ArticleGoogle Scholar
  32. Storey JD, Tibshirani R: Statistical significance for genomewide studies. Proc Natl Acad Sci USA. 2003, 100 (16): 9440-9445. 10.1073/pnas.1530509100.PubMed CentralPubMedView ArticleGoogle Scholar
  33. Lee HK, Braynen W, Keshav K, Pavlidis P: ErmineJ: tool for functional analysis of gene expression data sets. BMC Bioinformatics. 2005, 6: 269-10.1186/1471-2105-6-269.PubMed CentralPubMedView ArticleGoogle Scholar
  34. Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC, et al: Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet. 2001, 29 (4): 365-371. 10.1038/ng1201-365.PubMedView ArticleGoogle Scholar
  35. Kebschull M, Demmer R, Behle JH, Pollreisz A, Heidemann J, Belusko PB, Celenti R, Pavlidis P, Papapanou PN: Granulocyte chemotactic protein 2 (GCP-2/CXCL6) complements interleukin-8 in periodontal disease. J Periodontal Res. 2009, 44 (4): 465-471. 10.1111/j.1600-0765.2008.01134.x.PubMed CentralPubMedView ArticleGoogle Scholar
  36. Paster BJ, Olsen I, Aas JA, Dewhirst FE: The breadth of bacterial diversity in the human periodontal pocket and other oral sites. Periodontol 2000. 2006, 42: 80-87. 10.1111/j.1600-0757.2006.00174.x.PubMedView ArticleGoogle Scholar
  37. Kato S, Sugimura N, Nakashima K, Nishihara T, Kowashi Y: Actinobacillus actinomycetemcomitans induces apoptosis in human monocytic THP-1 cells. J Med Microbiol. 2005, 54 (Pt 3): 293-298. 10.1099/jmm.0.45693-0.PubMedView ArticleGoogle Scholar
  38. Shenker BJ, Demuth DR, Zekavat A: Exposure of lymphocytes to high doses of Actinobacillus actinomycetemcomitans cytolethal distending toxin induces rapid onset of apoptosis-mediated DNA fragmentation. Infect Immun. 2006, 74 (4): 2080-2092. 10.1128/IAI.74.4.2080-2092.2006.PubMed CentralPubMedView ArticleGoogle Scholar
  39. Shenker BJ, Hoffmaster RH, Zekavat A, Yamaguchi N, Lally ET, Demuth DR: Induction of apoptosis in human T cells by Actinobacillus actinomycetemcomitans cytolethal distending toxin is a consequence of G2 arrest of the cell cycle. J Immunol. 2001, 167 (1): 435-441.PubMedView ArticleGoogle Scholar
  40. Yilmaz O, Yao L, Maeda K, Rose TM, Lewis EL, Duman M, Lamont RJ, Ojcius DM: ATP scavenging by the intracellular pathogen Porphyromonas gingivalis inhibits P2X7-mediated host-cell apoptosis. Cell Microbiol. 2008, 10 (4): 863-875. 10.1111/j.1462-5822.2007.01089.x.PubMed CentralPubMedView ArticleGoogle Scholar
  41. Kinane DF, Galicia JC, Gorr SU, Stathopoulou PG, Benakanakere M: Porphyromonas gingivalis interactions with epithelial cells. Front Biosci. 2008, 13: 966-984. 10.2741/2736.PubMedView ArticleGoogle Scholar
  42. O'Brien-Simpson NM, Pathirana RD, Walker GD, Reynolds EC: Porphyromonas gingivalis RgpA-Kgp proteinase-adhesin complexes penetrate gingival tissue and induce proinflammatory cytokines or apoptosis in a concentration-dependent manner. Infect Immun. 2009, 77 (3): 1246-1261. 10.1128/IAI.01038-08.PubMed CentralPubMedView ArticleGoogle Scholar
  43. Wright HJ, Matthews JB, Chapple IL, Ling-Mountford N, Cooper PR: Periodontitis associates with a type 1 IFN signature in peripheral blood neutrophils. J Immunol. 2008, 181 (8): 5775-5784.PubMedView ArticleGoogle Scholar
  44. Tian Q, Stepaniants SB, Mao M, Weng L, Feetham MC, Doyle MJ, Yi EC, Dai H, Thorsson V, Eng J, et al: Integrated genomic and proteomic analyses of gene expression in Mammalian cells. Mol Cell Proteomics. 2004, 3 (10): 960-969. 10.1074/mcp.M400055-MCP200.PubMedView ArticleGoogle Scholar
  45. Prabhakar U, Conway TM, Murdock P, Mooney JL, Clark S, Hedge P, Bond BC, Jazwinska EC, Barnes MR, Tobin F, et al: Correlation of protein and gene expression profiles of inflammatory proteins after endotoxin challenge in human subjects. DNA and cell biology. 2005, 24 (7): 410-431. 10.1089/dna.2005.24.410.PubMedView ArticleGoogle Scholar
  46. Newman MG, Marinho VC: Assessing bacterial risk factors for periodontitis and peri-implantitis: using evidence to enhance outcomes. Compendium (Newtown, Pa). 1994, 15 (8): 958-972.Google Scholar

Copyright

© Papapanou et al; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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