Gingival Epithelial Cell Transcriptional Responses to Commensal and Opportunistic Oral Microbial Species

Transcriptional profiling and ontology tools were utilized todefine the biological pathways of gingival epithelial cellsmodulated by coculture with the oral commensal Streptococcusgordonii and the opportunistic commensal Fusobacterium nucleatum.Overall, F. nucleatum and S. gordonii perturbed the gingivalepithelial cell transcriptome much less significantly than theoral pathogens Porphyromonas gingivalis and Aggregatibacteractinomycetemcomitans perturbed the transcriptome, indicatingthat there was a greater degree of host adaptation by the commensalspecies (M. Handfield, J. J. Mans, G. Zheng, M. C. Lopez, S.Mao, A. Progulske-Fox, G. Narasimhan, H. V. Baker, and R. J.Lamont, Cell. Microbiol. 7:811-823, 2005). The biological pathwayssignificantly impacted by F. nucleatum and S. gordonii includedthe mitogen-activated protein kinase (MAPK) and Toll-like receptorsignaling pathways. Differential regulation of GADD45 and DUSP4,key components of the MAPK pathway, was confirmed at the proteinlevel by Western blotting. Modulation of the MAPK pathway islikely to affect host cell proliferation and differentiation.In addition, both the MAPK and Toll-like receptor pathways ultimatelyconverge on cytokine gene expression. An enzyme-linked immunosorbentassay of secreted interleukin-6 (IL-6) and IL-8 demonstratedthat F. nucleatum induced production of these cytokines, whereasS. gordonii inhibited secretion from the epithelial cells. Stimulationof secretion of proinflammatory cytokines from epithelial cellsmay reflect the invasive phenotype of F. nucleatum and contributeto the greater pathogenic potential of F. nucleatum than ofS. gordonii.

INTRODUCTION

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INTRODUCTION

Humans are hosts of a complex and abundant population of microorganismsthat colonize the mucosal membranes and skin. This commensalmicrobiota is an integral component of a complex homeostasismechanism that impedes the activity of pathogenic microorganisms(6, 24). At optimal composition, commensals can prevent boththe attachment and into the multiplication of pathogens andtheir invasion of epithelial cells and into the circulation.In addition, commensals supply essential nutrients, regulateepithelial development, and contribute to the maturation andmaintenance of the immune system (5, 11, 25, 43). Thus, commensalinhabitants have become host adapted during a long evolutionaryrelationship. Under certain conditions, however, a subset ofspecies can escape host restraint mechanisms and initiate disease,and these species have been called opportunistic commensals.There is increasing evidence that the innate immune system maydiscriminate among commensals, opportunistic commensals, andovert pathogens, and it has been suggested that this discriminationcontrols the balance between microbial intrusion and host integrity(21, 40, 50, 51, 55).

More than 700 species, or phylotypes, of bacteria can inhabitthe oral cavity (1). Temporally distinct patterns of microbialcolonization result in biofilm formation on all surfaces inthe oral cavity. On the tooth surfaces, the initial colonizersof the dental plaque biofilm are principally oral streptococciand actinomyces. Establishment of these organisms facilitatesthe subsequent colonization of additional actinomyces and relatedgram-positive rods along with gram-negative bacteria, such asFusobacterium nucleatum. Further maturation is characterizedby colonization by gram-negative anaerobes, such as Porphyromonasgingivalis (34, 52). Once colonization of the subgingival areahas occurred, organisms shed from the plaque biofilm can interactwith host epithelial cells that both have a barrier functionand act as sensors of microbial infection (28). While many commonoral organisms can adhere to gingival epithelial cells, onlya subset of these organisms, including F. nucleatum, Aggregatibacter(Actinobacillus) actinomycetemcomitans, and P. gingivalis, caninvade host cells (22, 23, 39, 59). Although it is well establishedthat the bacterial inhabitants of the subgingival crevice aredirect precursors of periodontal disease, the oral microbiotaincludes a spectrum ranging from commensals, such as Streptococcusgordonii, to aggressive pathogens, such as P. gingivalis. Somespecies, such as F. nucleatum, are located near the center ofthis spectrum as opportunistic commensals and are frequentlyfound in individuals with good oral health but are also potentiallyable to contribute to disease (4, 15, 17, 44, 58, 62).

Transcriptional profiling using microarrays provides a way tomonitor host cell responses to colonizing microorganisms ona global scale (8, 41). Numerous innate immune factors, forexample, have been consistently found to be differentially regulatedin host cells infected with pathogenic organisms compared tothe regulation in uninfected controls (23, 29, 42). In the oralcavity, the pathogens P. gingivalis and A. actinomycetemcomitansinduce widespread changes in the gingival epithelial cell transcriptomethat are largely organism specific (23). There have been onlya limited number of studies of the transcriptional responsesto commensal organisms, and in these studies the focus is almostexclusively on nonoral mucosal ecosystems. For example, a studyof the gastrointestinal tract commensal Bacteroides thetaiotaomicronshowed it can modulate expression of genes involved in severalimportant intestinal functions, including nutrient absorption,mucosal barrier fortification, xenobiotic metabolism, angiogenesis,and postnatal intestinal maturation (25). Commensal bacterialreconstitution of germfree mice has been shown to up-regulateexpression of colonic epithelial cell genes associated withgrowth, apoptosis, and immune responses (19). In contrast, genesthat may participate in extracellular oxidant defense and cellularmetabolism were down-regulated by a nonpathogenic bacterialchallenge (19). Hence, there is evidence that the host celltranscriptional response can be specific for the infecting organismand that commensals and pathogens can regulate distinct physiologicalfunctions in host cells.

In this study we utilized expression microarrays to investigatethe transcriptional responses of oral epithelial cells to challengewith the commensal S. gordonii and the opportunistic commensalF. nucleatum. The transcriptional responses induced by theseorganisms were very similar to each other yet significantlydifferent than the responses reported previously for oral pathogens(23). Among the biological processes altered most significantlyin the host cells was the signal transduction pathway associatedwith mitogen-activated protein kinase (MAPK) and downstreameffector molecules, including interleukins. Understanding howthe host has adapted to commensals and how barrier cells respondto limit the impact of commensals should provide a mechanisticbiological basis for health in the mixed bacterium-human ecosystemof the oral cavity.

MATERIALS AND METHODS

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MATERIALS AND METHODS

Bacterial strains and culture conditions. F. nucleatum ATCC 25586 was cultured anaerobically at 37°Cin Trypticase soy broth supplemented with yeast extract (1 mgml^–1), hemin (5 µg ml^–1), and menadione (1µg ml^–1). S. gordonii DL-1 was cultured anaerobicallyat 37°C in Todd-Hewitt broth with 0.5% yeast extract andglucose (0.5%).

Epithelial cells. Human immortalized gingival keratinocytes (HIGK) were originallygenerated by transfection of primary gingival epithelial cellswith E6/E7 from human papillomavirus (45). HIGK were culturedin the presence of 5% CO₂ in keratinocyte serum-free medium(Gibco/Invitrogen, Carlsbad, CA) supplemented with 0.05 mM calciumchloride, 200 mM L-glutamine, and an antibiotic-antimycotic(Invitrogen).

Microbe-host cell coculture. Bacteria in the mid-log phase were harvested and washed by centrifugationand then resuspended in antibiotic-free keratinocyte serum-freemedium. HIGK (10⁷ cells) were cocultured with bacteria to obtaina total association (adherent plus invading bacteria) of approximately100 bacteria per epithelial cell. The numbers of adherent andinvading organisms were confirmed in parallel experiments byplate counting (23). After 2 h of incubation at 37°C inthe presence of 5% CO₂, the HIGK were lysed with Trizol (Invitrogen)prior to RNA extraction. Cocultures were carried out in quadruplicate.

RNA isolation, cDNA-cRNA synthesis, and chip hybridization. RNA isolation, cDNA synthesis, labeled cRNA synthesis, and chiphybridization were performed as previously described (23). Briefly,total RNA was extracted from Trizol-lysed cells, treated withDNase I, purified, and quantified by using standard methods(QIAGEN, Valencia, CA, and Affymetrix, Santa Clara, CA). cDNAwas synthesized by using the Affymetrix protocol (SuperScriptdouble-stranded cDNA synthesis kit; Invitrogen); 5 to 8 µgof total cellular RNA was used as a template to amplify mRNAspecies for detection. Double-stranded cDNA was purified andused as a template for labeled cRNA synthesis. In vitro transcriptionwas performed using a BioArray high-yield RNA transcript labelingkit (T7; Enzo Life Science, Farmingdale, NY) to incorporatebiotinylated nucleotides. cRNA was subsequently fragmented andhybridized on Genechip human genome U133-A oligonucleotide arrays(Affymetrix) with proper controls. RNA samples were not pooled.The microarrays were hybridized for 16 h at 45°C, stainedwith phycoerythrin-conjugated streptavidin, and washed usingthe Affymetrix protocol (EukGE-WS2v4) with an Affymetrix fluidicsstation, and then they were scanned with an Affymetrix GeneChip3000 scanner.

Microarray data analysis. Microarray data analysis was performed as previously described(23, 41). Briefly, expression filters were applied to removeAffymetrix controls and probe sets whose signals were not detectedin all samples. The signal intensity values in the resultingdata set were variance normalized, mean centered, and rankedby their coefficients of variation. Normalization was performedto give equal weight to all probe sets in the analysis, regardlessof the order of magnitude of the raw signal intensity. To reducethe confounding effect of background signal variation on theanalysis, only the half of the data set exhibiting the mostvariation across samples was used to perform an unsupervisedhierarchical cluster analysis using the Cluster software (16).The resulting heat map and cluster dendrograms were visualizedwith the Treeview software (16) to determine the extent of characteristichost cell responses to each infection state, defined as identicaltreatments clustering together. Additional quality control datafor the arrays are provided in the supplemental material.

Following the initial assessment of the host cell response toeach condition, a supervised analysis was performed to investigatedifferences in gene regulation among experimental conditions.For this analysis, the raw signal intensities were log transformedfor all probe sets that passed the initial expression filtersand were correlated using BRB Array Tools (R. Simon and A. Peng-Lam,National Cancer Institute, Rockville, MD). In each supervisedanalysis, biological replicates were grouped into classes basedon their infection states during coculture experiments, andprobe sets significant at the P <0.001 level for the classwere identified. To test the abilities of the significant probesets to truly distinguish between the classes, leave-one-outcross-validation (LOOCV) studies were performed. In these LOOCVstudies each array was left out in turn and a classifier wasderived for the three groups by selecting probe sets significantat a P value of <0.001. The significant probe sets were thenused with several prediction models (compound covariate predictor,nearest-neighbor predictor, and support vector machine predictor)to predict the class identity of the array that was left outand not included when the classification model was constructed.The ability of the classifier to correctly predict the classidentity of the array that was left out was estimated usingMonte Carlo simulations with 2,000 permutations of the dataset.

Functional categorization by gene ontology and bioinformatics analyses. Gene ontology trees were populated using Pathway Express (33),available at http://vortex.cs.wayne.edu/projects.htm.

Immunoblotting. HIGK were infected with F. nucleatum or S. gordonii as describedabove, using a time course of 1, 2, or 6 h. Cells were washedthree times with phosphate-buffered saline and lysed in radioimmunoprecipitationbuffer with proteinase inhibitors (Sigma, St. Louis, MO). Twenty-fivemicrograms of protein was separated by 10% sodium dodecyl sulfate-polyacrylamidegel electrophoresis, electrophoretically transferred to polyvinylidenedifluoride membranes (Millipore, Billerica, MA), and blockedwith 5% skim milk in Tris-buffered saline-0.1% Tween 20. Themembranes were incubated for 1 h with primary antibodies toGADD45 ${alpha}$ (1:200; Santa Cruz Biotechnology, Santa Cruz, CA), GADD45ß(1:200; Santa Cruz Biotechnology), DUSP4 (1:2,000; Abcam, Cambridge,MA), and ß-actin (1:10,000; Abcam). After three washesin Tris-buffered saline-0.1% Tween 20, each membrane was reactedwith the species-appropriate peroxidase-coupled secondary antibody(1:1,000). Visualization was performed with the enhanced chemiluminescencesystem (Amersham, Buckinghamshire, United Kingdom). Band intensitieswere scanned and quantified using the Kodak 1D image analysissoftware (v.3.6.1).

Detection of cytokines. Supernatants of HIGK infected with F. nucleatum or S. gordoniiwere collected and filter sterilized. Interleukin-6 (IL-6) andIL-8 concentrations were determined by an enzyme-linked immunosorbentassay (Quantikine, Minneapolis MN) performed according to themanufacturer's protocol. Experiments were conducted in triplicate.

Microarray data accession numbers. The array results have been deposited in the GEO repository(http://www.ncbi.nlm.nih.gov/geo/index.cgi) under accessionnumbers GSM159477 to GSM159488, series GSE6927.

RESULTS

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RESULTS

Association of F. nucleatum and S. gordonii with HIGK. F. nucleatum and S. gordonii exhibited different capacitiesto invade HIGK. At a concentration of approximately 100 organismsper epithelial cell, 55% of F. nucleatum cells were recoveredintracellularly, whereas S. gordonii remained essentially extracellular(99.5%).

Comparison of the transcriptional profiles of HIGK infected with F. nucleatum and HIGK infected with S. gordonii. All samples of uninfected HIGK, F. nucleatum-infected cells,and S. gordonii-infected cells were used to determine the overallsimilarity of the transcriptional responses. After eliminationof probe sets whose signals were not greater than backgroundlevels on all arrays, signal intensity data for the 12,125 probesets that passed the initial expression filters were includedin an unsupervised cluster analysis and supervised class predictionanalysis. The unsupervised hierarchical cluster analysis revealedan infection state-dependent host cell transcriptional profile,as biological replicates clustered together (data not shown).In a supervised analysis based on the infection state, at asignificance level of P < 0.001, 240 probe sets were differentiallyexpressed. Assuming normality of the data set, the 240 significantgenes is 20-fold higher than the 12 probe sets that would beexpected by chance at a significance threshold of P < 0.001,given that 12,125 probe sets passed the expression filter. Treeviewvisualization of the 240 probe sets differentially expressedin the three classes is shown in Fig. 1. The major separationnode occurred between uninfected cells and infected organisms,independent of bacterial species, and the differences betweenthe uninfected and infected states exceeded the observable differencesbetween F. nucleatum and S. gordonii, although organism-specificgene signatures could be discerned. Overall, 84% of the probesets were regulated similarly by F. nucleatum and S. gordonii,which may reflect the degree of adaptation to the host of boththese species.

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FIG. 1. Hierarchical clustering of variance-normalized gene expression data. The expression pattern of the cRNAs analyzed by using microarrays was determined by a supervised analysis of the variance-normalized data set of differentially expressed genes (P < 0.001, BRB ArrayTools) with the algorithm Cluster and was displayed with Treeview. Each row represents an individual gene element on the array, and each column represents the expression states of cRNAs for the challenge condition indicated. Each expression data point represents the relative fluorescence intensity of the cRNA from F. nucleatum-infected cells (columns Fn R1 to Fn R4) or S. gordonii-infected cells (columns Sg R1 to Sg R4) to the fluorescence intensity of the cRNA from uninfected cells (columns CTRL R1 to CTRL R4). The distance matrix used to determine the relatedness of samples through gene expression space was 1 – Pearson's correlation coefficient. The cluster is subdivided into three groups consisting of genes that were repressed (green), genes that were induced (red), and genes whose expression did not change (black). The variation in gene expression for a given gene is expressed as the distance from the mean observation for that gene according to the color scale below the heat map.

Ontology analysis. While it is becoming increasingly clear that the predictivepower of regulation of individual genes is limited due to theextensive interconnectivity among regulatory networks, the assemblyof regulated genes into biologically relevant pathways by ontologyanalysis has greater biological resolution. Therefore, in orderto mine the array data for biologically relevant information,an ontology analysis of known metabolic pathways was performedusing individual comparisons of the F. nucleatum-infected stateor the S. gordonii-infected state with the corresponding baselineuninfected state. A pairwise comparison was performed by usingthe ontology algorithms described above. For this analysis,signal intensities were renormalized across all samples, andsupervised analyses were repeated as described above. In F.nucleatum-infected HIGK, 11,909 genes passed the initial expressionfilters, while 11,835 genes were analyzed for S. gordonii-infectedcells. At a significance level of P < 0.001, 145 genes weredifferentially expressed in the F. nucleatum-infected cellscompared to the expression in uninfected controls, whereas 268genes were differentially expressed in the S. gordonii-infectedcells. At a less stringent significance level, P < 0.05,class prediction analysis revealed 1,917 F. nucleatum-regulatedgenes and 2,910 S. gordonii-regulated genes. The abilities ofprobe sets with significance at P < 0.001 or at P < 0.05to correctly identify treatment groups were confirmed by LOOCVanalysis. The classifiers performed flawlessly and correctlypredicted the treatment group with 100% accuracy. To populatebiological pathways to the maximal extent and thus enhance theirpredictive power, probe sets at a significance level of P <0.05 were analyzed by the Pathway Express algorithm (12-14,31-33). Table 1 shows the most impacted epithelial pathwaysgenerated in this analysis in order of their impact factors.Pathways impacted by F. nucleatum or S. gordonii showed considerableoverlap, and the MAPK pathway and the Toll-like receptor signalingpathway were two of the top three most affected pathways forboth organisms. Thus, recognition of and response to these oralcommensals by gingival epithelial cells involve the "classical"system of microbe-associated molecular pattern-pattern recognitionreceptor binding and signaling propagation through MAPK pathways.Coupled with the absence of a danger signal (46), this interactionmay maintain a physiologic balance between the host and theorganism.

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TABLE 1. Ontology analysis of epithelial cell pathways impacted by infection with F. nucleatum or S. gordonii^a

Correlation between mRNA and protein levels in the MAPK pathway. As the MAPK pathway was significantly (P < 0.001) impactedat the transcriptional level by both organisms, we investigatedthe correlation between mRNA and protein levels for three ofthe most statistically significantly regulated genes. Genesencoding GADD45 ${alpha}$ and GADD45ß were transcriptionallyup-regulated following F. nucleatum infection (P = 0.0005 andP < 10^–6, respectively), whereas expression of thegene encoding DUSP4 (one of several MAPK phosphatase familymembers) was down-regulated (P < 10^–6). S. gordoniiup-regulated GADD45ß gene expression (P < 10^–6),down-regulated DUSP4 gene expression (P < 10^–6), andhad no detectable effect on GADD45 ${alpha}$ gene expression. The proteinexpression data obtained by Western blotting (Fig. 2) were consistentwith the transcriptional data. Both F. nucleatum and S. gordoniidecreased the levels of DUSP4; F. nucleatum up-regulated GADD45 ${alpha}$ and GADD45ß expression; and S. gordonii infectionresulted in an increase in the GADD45ß level. In general,the amount of regulated protein continued to change over a 6-hperiod, corroborating the predictive power of the transcriptional"snapshot" for the longer-term phenotype of the epithelial cells,at least for these genes. The roles of these genes in signaltransduction through the MAPK pathway and their impact on cellphysiology are shown diagrammatically in Fig. 3.

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FIG. 2. Western immunoblots of HIGK infected with F. nucleatum (Fn) or S. gordonii (Sg) and uninfected controls (CTRL) for 1, 2, or 6 h. The blots were probed with antibodies to GADD45 ${alpha}$ (upper panel), GADD45ß (middle panel), and DUSP4 (lower panel) and then stripped and reprobed with antibodies to ß-actin. The graphs show the results of densitometric analyses of the ratio of test protein band intensity to ß-actin band intensity.

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FIG. 3. MAPK-related pathways containing F. nucleatum and S. gordonii differentially regulated genes at P < 0.05, adapted from Pathway Express and using the Kyoto Encyclopedia of Genes and Genomes nomenclature (see text for details). Red indicates up-regulation, green indicates down-regulation, and gray indicates no change in expression. +P indicates phosphorylation, and –P indicates dephosphorylation. An arrow indicates a molecular interaction resulting in activation, and a line without an arrowhead indicates a molecular interaction resulting in inhibition. e, expression.

Correlation between mRNA and secreted cytokines. Both the MAPK and Toll-like pathways can converge on expressionof cytokines. While recognizing that control of cytokine secretionis hierarchical and can occur at the transcriptional and posttranscriptionallevels, we assessed the phenotypic significance of the ontologyanalysis by directly assaying at the protein level the proinflammatorycytokines IL-6 and IL-8. Secretion of IL-6 and IL-8 followinginfection with F. nucleatum or S. gordonii was monitored overtime (Fig. 4). After 4 h of coculture, F. nucleatum stimulatedsecretion of IL-8, which is consistent with previous reports(9, 26, 36), and of IL-6. In contrast, S. gordonii reduced thelevels of secreted IL-6 and IL-8 compared to the levels in uninfectedcontrol cells over an 8-h incubation. Inhibition of proinflammatorycytokine secretion by S. gordonii may eliminate initiation ofa potentially destructive immune response and contribute tothe maintenance of oral health.

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FIG. 4. Enzyme-linked immunosorbent assay of IL-6 (A) and IL-8 (B) accumulation in HIGK supernatants following coculture with F. nucleatum or S. gordonii for 2, 4, 6, or 8 h. Ctrl, uninfected control. The error bars indicate standard deviations (n = 3). One asterisk, P < 0.05; two asterisks, P < 0.001 (as determined by a t test).

DISCUSSION

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DISCUSSION

The epithelial cells that line the gingival crevice form theinitial interactive interface between the host and subgingivalbacteria. The bacterial inhabitants of this area are diverseand have a spectrum of pathogenic potentials, ranging from commensal(such as S. gordonii) through opportunistic commensal (suchas F. nucleatum) to overtly pathogenic (such as P. gingivalisand A. actinomycetemcomitans). The outcome of the bacterium-epithelialcell interaction, in which the host cells distinguish the infectingbacteria and tailor a response while the bacteria attempt tomanipulate host cell responses, is an important component inestablishing pathogenic potential. Transcriptional profilingprovides insights into these extremely complex systems. In aprevious study Handfield et al. (23) reported that HIGK responsesto the pathogens P. gingivalis and A. actinomycetemcomitansreverberated throughout the transcriptome and impacted biologicalpathways, including cell development and morphogenesis pathways.In contrast, the HIGK response to the less pathogenic organismsF. nucleatum and S. gordonii was much more restrained. The similartranscriptional responses to F. nucleatum and S. gordonii provideexperimental support for the concept that commensals and opportunisticcommensals have developed a more balanced evolutionary relationshipwith the host than pathogens have developed (17, 24). Futurestudies will be directed toward trying to identify host cellgenes and pathways that can be used to predict a commensal orpathogenic challenge. Such genes and pathways could be usedas markers to assess the pathogenic potentials of the hundredsof species or complex mixtures of subgingival bacteria for whichno virulence information or culture methods are available (38).An additional distinction between the gingival cell responsesto more pathogenic and less pathogenic organisms was that thecore common transcriptional responses to P. gingivalis and A.actinomycetemcomitans were very limited (around 15% of the regulatedgenes), and organism-specific responses predominated (23). However,the core transcriptional responses to F. nucleatum and S. gordoniiwere more extensive, with 84% of the genes similarly regulatedby these two organisms, including the genes phenotypically validatedand described here. Considering that F. nucleatum possessesan outer membrane while S. gordonii does not and that F. nucleatumis orders of magnitude more invasive than S. gordonii, it appearsthat transcriptional responses are not strictly dependent onthe physical nature of the bacterial microbe-associated molecularpatterns or on the extra- or intracellular location of the bacteria.

One pathway that was significantly impacted by F. nucleatum(P = 0.00825) and S. gordonii (P = 0.00437) involves MAPK signaltransduction. This signaling pathway, which is evolutionarilyconserved, connects cell surface receptors to regulatory targetswithin cells. MAPK signaling is involved in a vast array ofphysiological processes, including cell growth, migration, proliferation,differentiation, survival, development, and innate immunity(37, 49, 64). Consistent with these diverse roles, a varietyof stimuli can activate MAPK pathways; these stimuli includegrowth factors, cytokines, ligands for G protein-coupled receptors,transforming agents, environmental stress, and viral and bacterialligands. In the MAPK pathway, expression of GADD45 ${alpha}$ and GADD45ßwas up-regulated by F. nucleatum infection, whereas expressionof DUSP4 was down-regulated. Similarly, S. gordonii up-regulatedGADD45ß expression and down-regulated DUSP4 expression.Regulation occurred at both the mRNA and protein levels. TheGADD45 gene was originally identified as a gene that is rapidlyinduced by agents that cause DNA damage (18, 48). Transcriptionalregulation of the GADD45 gene is mediated by both p53-dependentand p53-independent mechanisms (56), and GADD45 family members( ${alpha}$ , ß, and ${gamma}$ ) are involved in the activation of thep38 and JNK pathways through MEKK4. Up-regulation of GADD45expression may ultimately converge on growth arrest and on theactivation of the nuclear transcription factor NF- ${kappa}$ B (47, 56,57, 63). The dual-specificity phosphatase DUSP4 is involvedin the inactivation of the MAPKs by dephosphorylating both Thrand Tyr residues of ERK1 and ERK2 (30). One role of the DUSPfamily of phosphatases may be in the postinduction repressionof MAPK activity (60). Hence, both up-regulation of GADD45 expressionand down-regulation of DUSP4 expression by F. nucleatum or S.gordonii engender the concordant phenotype of enhanced informationflow through the MAPK pathway. Together, these results providea mechanistic framework for previous reports which showed thatF. nucleatum and S. gordonii activated MAPK and NF- ${kappa}$ B in gingivalepithelial cells (7, 9, 26, 27, 35, 61).

One of the downstream targets of MAPK signaling is the productionof cytokines. F. nucleatum induced IL-6 and IL-8 secretion,whereas S. gordonii repressed secretion of both of these proinflammatorycytokines. Differences in the cytokine secretion profiles maybe related to differential regulation of individual componentsof the MAPK pathway, such as the ability of F. nucleatum toup-regulate both GADD45 ${alpha}$ expression and GADD45ß expression.In addition, F. nucleatum, but not S. gordonii, down-regulatedexpression of RSK2, whereas S. gordonii, but not F. nucleatum,down-regulated expression of Sap1a. RSK2 is a serine/threoninekinase that may play a role in mediating the growth factor-and stress-induced activation of the transcription factor CREB.SAP1a is a nuclear protein that stimulates transcription viathe c-fos serum response element and also via an Ets bindingsite independent of the serum response factor. However, althoughRSK2 and Sap1a are involved in distinct aspects of MAPK signaling,their precise physiological roles are not known. The inductionof expression of proinflammatory cytokines in gingival tissuesand the subsequent inflammatory tissue damage are consideredcontributory factors in the pathogenesis of periodontal disease(10, 20, 53). Hence, stimulation of IL-6 and IL-8 by F. nucleatummay be one property that increases the pathogenicity of thisorganism compared to the pathogenicity of S. gordonii. Indeed,a general hyporesponsiveness to commensals may be advantageousin order to limit tissue destruction that might occur if a strongproinflammatory response were induced. Furthermore, it has beenproposed that commensal species can "program" host cells tolimit subsequent responses to more pathogenic organisms (2,54) Conversely, in gingival epithelial cells, prior infectionwith the pathogen P. gingivalis can paralyze the local chemokineresponse to F. nucleatum (9). The extent to which challengewith S. gordonii can modulate subsequent host cell responsesto other organisms is currently being investigated. Of possiblygreater in vivo relevance is the potential of a complex microbialcommunity to impact the transcriptional profile and phenotypicresponses of host cells in a manner distinct from the summedactivities of its constituents. Such community-based responsescould partially explain apparent discrepancies that are observedbetween certain oral clinical manifestations and the putativepathogenic potentials of causative microbial species. For example,it is generally recognized that clinical cases of localizedaggressive periodontitis that are associated with proapoptoticand proinflammatory A. actinomycetemcomitans do not result insignificant inflammation or gingival destruction (3). It istempting to speculate that the anti-inflammatory nature of certaincommensal species has the potential to restrain the proinflammatorycapability of pathogenic species.

ACKNOWLEDGMENTS

This study was supported by grants DE11111 (R.J.L.), DE16715(M.H.), and T32 DE7200 (J.J.M.).

FOOTNOTES

* Corresponding author. Mailing address: Department of Oral Biology and Center for Molecular Microbiology, College of Dentistry, University of Florida, Gainesville, FL 32610-0424. Phone: (352) 846-0763. Fax: (352) 392-2361. E-mail: mhandfield{at}dental.ufl.edu

Published ahead of print on 16 February 2007.

Supplemental material for this article may be found at http://iai.asm.org/.

Editor: J. B. Bliska

REFERENCES

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