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Beyond Good and Evil in the Oral Cavity: Insights into Host-Microbe Relationships Derived from Transcriptional Profiling of Gingival Cells
1 Department of Oral Biology and Center for Molecular Microbiology, College of Dentistry, Box 100424 JHMHSC, and Correspondence: * corresponding author, mhandfield{at}dental.ufl.edu
In many instances, the encounter between host and microbial cells, through a long-standing evolutionary association, can be a balanced interaction whereby both cell types co-exist and inflict a minimal degree of harm on each other. In the oral cavity, despite the presence of large numbers of diverse organisms, health is the most frequent status. Disease will ensue only when the host-microbe balance is disrupted on a cellular and molecular level. With the advent of microarrays, it is now possible to monitor the responses of host cells to bacterial challenge on a global scale. However, microarray data are known to be inherently noisy, which is caused in part by their great sensitivity. Hence, we will address several important general considerations required to maximize the significance of microarray analysis in depicting relevant host-microbe interactions faithfully. Several advantages and limitations of microarray analysis that may have a direct impact on the significance of array data are highlighted and discussed. Further, this review revisits and contextualizes recent transcriptional profiles that were originally generated for the specific study of intricate cellular interactions between gingival cells and 4 important plaque micro-organisms. To our knowledge, this is the first report that systematically investigates the cellular responses of a cell line to challenge by 4 different micro-organisms. Of particular relevance to the oral cavity, the model bacteria span the entire spectrum of documented pathogenic potential, from commensal to opportunistic to overtly pathogenic. These studies provide a molecular basis for the complex and dynamic interaction between the oral microflora and its host, which may lead, ultimately, to the development of novel, rational, and practical therapeutic, prophylactic, and diagnostic applications.
Key Words: microarray • transcriptional profiling • oral epithelium • commensal • pathogen • transcriptomic
The human oral cavity is a complex ecosystem that contains a large number of bacterial colonizers that thrive in a dynamic environment. Since health is the most common state of a host, it has been speculated that the autochthonous flora and the host have co-evolved and interact in a balanced fashion that is beneficial to both the host and the microbiota (Marsh, 2003). Although such benefits are not well-defined in the oral cavity, in an analogous situation, indigenous bacteria of the GI tract provide an appreciable number of documented benefits to the host, including, for example, the generation of simplified carbohydrates, amino acids, and vitamins; the prevention of infection by pathogens through direct competition for niches or by immune cross-reactivity; the stimulation of vascularization and development of intestinal vili; and the enhancement of the normal development of the immune system (Hooper et al., 2001; Gilmore and Ferretti, 2003). Thus, colonizing organisms have the potential to affect the normal physiological status and development of the epithelium through modulation of host gene expression. Study of the effect of the oral flora on the oral epithelium in the gingival crevice is less advanced; however, emerging work suggests a role in stimulating the host innate immune response (Darveau et al., 1998; Dixon et al., 2004). Since host and microbiota interactions are inherently unstable, disease may arise in the oral cavity of a susceptible host when a perturbation occurs at the subgingival interface between host and bacteria.
The etiology of oral infectious diseases is complex and involves consortia of bacteria working in concert with immunological susceptibilities in the host. Colonization of the subgingival area initially depends on extension of the supragingival plaque biofilm below the gumline, whereupon it becomes subgingival plaque. The subgingival area is less oxygenated, and this—in combination with the metabolic activity of the initial colonizers, such as the streptococci—reduces the oxygen tension and allows anaerobes to survive. Early-colonizing streptococci such as S. gordonii generally do not cause disease in the oral cavity, but are capable of causing disease at systemic sites such as on defective heart valves. While the relative proportion of streptococci decreases as subgingival plaque matures, the total number of these organisms remains high (Socransky et al., 1998; Ximenez-Fyvie et al., 2000; Aas et al., 2005; Quirynen et al., 2005). A predominant anaerobic species in the subgingival biofilm is Fusobacterium nucleatum, a Gram-negative organism that is prevalent in mature plaque in both health and disease (Dzink et al., 1988; Tanner and Bouldin, 1989; Moore and Moore, 1994), and thus is considered an opportunistic commensal. The presence of Streptococcus gordonii and F. nucleatum favors colonization by later, more pathogenic organisms, such as Porphyromonas gingivalis, which plays a role in the initiation and progression of chronic periodontitis. Another later-pathogenic colonizer is Aggregatibacter actinomycetemcomitans, a causal agent of the clinically distinct localized aggressive periodontitis (LAP). However, while bacteria have traditionally been viewed as beneficial (good) or harmful (evil), it is our contention that these designations are no longer useful. In his pivotal work, Beyond Good and Evil, Nietzsche (2001) explored the concept of abandoning traditional morality in favor of a perspectival view of the nature of knowledge. Simply stated, "it is we alone who have fabricated causes...motive and purpose". Similarly, we should progress beyond the traditional concept of bacteria as good or bad, and rather embrace a contextual view of relative potential pathogenicity. Transcriptional profiling specifically allows the host to report the level of disruption induced by bacteria to affect host cells in the absence of preconceived notions regarding bacterial "intentions". The epithelial cells that line the gingival crevice constitute the initial interface between potential periodontopathic organisms, such as P. gingivalis and A. actinomycetemcomitans, and the host. However, commensal organisms, including S. gordonii, also have the opportunity to interact with gingival epithelial cells (Socransky et al., 1998; Ximenez-Fyvie et al., 2000). Epithelial cells recovered from the oral cavity show high levels of intracellular P. gingivalis, A. actinomycetemcomitans, and streptococci (Rudney et al., 2001, 2005; Colombo et al., 2007). Consequently, it can be hypothesized that the regulation of normal host cell physiological processes by these bacteria may be key to a balanced long-standing co-existence, and thus may also provide putative targets for therapeutic intervention (von Gruenigen et al., 1998; Habib et al., 1999; Wu, 2003). Both A. actinomycetemcomitans and P. gingivalis affect host epithelial cell signaling pathways, including those that funnel through nuclear transcription factors. Moreover, many oral organisms—including A. actinomycetemcomitans, P. gingivalis, F. nucleatum, and S. gordonii—have been shown to modulate expression of individual genes in epithelial cells (Meyer et al., 1991, 1996, 1997; Lamont et al., 1995; Meyer and Fives-Taylor, 1997; Darveau et al., 1998; Korostoff et al., 1998, 2000; Lamont and Jenkinson, 1998; Belton et al., 1999; Fives-Taylor et al., 1999; Holt et al., 1999; Shenker et al., 1999, 2000, 2001; Haraszthy et al., 2000; Guthmiller et al., 2001; Nakhjiri et al., 2001; Zhang et al., 2001, 2004a, Zhang et al., b; Song et al., 2002; Yilmaz et al., 2002, 2003; Noguchi et al., 2003; Cao et al., 2004). Thus, epithelial cells are capable of sensing and responding to oral bacteria at the transcriptional level. However, it is important not to lose sight of the fact that, despite the pathogenic potential of individual species, periodontal lesions are mixed infections, and the contributions of specific organisms to disease status are difficult to assess. Moreover, mixtures of organisms can be more pathogenic than single species (Ebersole et al., 1997; Kesavalu et al., 1998). Conversely, the presence of certain species, such as streptococci, can be antagonistic to others, such as A. actinomycetemcomitans (Hillman and Socransky, 1982; Hillman et al., 1985). While such synergistic and antagonistic interactions can occur at the bacteria-bacteria level, the impact of mixed microbial challenges on epithelial cell transcriptional responses has received little attention. Studies have shown, however, that P. gingivalis can antagonize the ability of Fusobacterium nucleatum to stimulate IL-8 and ICAM-1 (Darveau et al., 1998; Huang et al., 2001). Thus, the composition and timing of microbial challenge could have significant implications for epithelial cell transcriptional activity.
As mentioned above, epithelial cells are among the first cell types encountered by a microbe of the mucosal surface. In particular, keratinocytes are the main cell type in gingival epithelial tissues (Suchett-Kaye et al., 1998; Asai et al., 2001; Huang et al., 2001; Pleguezuelos et al., 2005). In addition to their barrier function, these cells also actively sense and signal the presence of bacteria and mobilize innate and specific defense mechanisms (Ichikawa et al., 2000; Lory and Ichikawa, 2002; Hybiske et al., 2004; Mans et al., 2006). Thus, it is increasingly appreciated that epithelial tissues such as the gingival epithelia are not merely passive barriers to infection, but have a proactive role in immune responses and the development of localized inflammatory conditions, such as periodontitis. The completion of the human genome sequence has ushered in a new era in the study of host-pathogen interactions. It is now possible to monitor the responses of host cells to bacterial challenge on a global scale. Expression profiling based on DNA microarrays (gene chips) permits the identification of pathways that are mobilized by the host in response to an invading organism. With a combination of expression profiling performed on human DNA microarrays and challenge with microbial mutant strains, it is now possible to characterize the role of individual bacterial virulence factors in the recognition, response, and subsequent mobilization of host responses (Cummings and Relman, 2000; Kellam, 2000; Manger and Relman, 2000; Kato-Maeda et al., 2001; Kellam and Weiss, 2006). As recently reviewed (Mans et al., 2006), microarrays can be used to monitor the molecular dialogue between host and bacterial cells and allow the epithelial cells to "describe" their responses to individual bacteria and to specific bacterial molecules. To date, the use of different sets of arrays and experimental systems in different studies has precluded a direct comparison of the genes discovered with similar organisms. Nevertheless, interesting similarities have been observed, and the modulation of a large number of induced or repressed genes was found in all systems tested (Cummings and Relman, 2000; Kellam, 2000; Manger and Relman, 2000; Kagnoff and Eckmann, 2001; Kato-Maeda et al., 2001; Yowe et al., 2001; Kellam and Weiss, 2006). Human microarrays have also been used to determine the transcriptional responses of gingival epithelial cells to co-culture with oral microbiota. In particular, transcriptional profiling, bioinformatics, and statistical and ontology tools have been used to uncover and dissect genes and pathways of human gingival epithelial cells that are modulated upon interaction with important oral organisms that have specific pathogenic personalities. A. actinomycetemcomitans and P. gingivalis are considered more aggressive pathogens, although these organisms can also be present in the absence of disease. F. nucleatum is considered more of an opportunistic commensal that may participate in the disease process when environmental conditions allow. S. gordonii generally does not directly contribute to the periodontal disease process. In addition, these organisms are representative of distinct temporal stages in the development of the subgingival biofilm: early - S. gordonii; mid - F. nucleatum; and late - P. gingivalis and A. actinomycetemcomitans.
Early investigations described above demonstrated the promise and potential of this new technology as a major research tool in the biological sciences. Unfortunately, some early studies also served to illustrate potential pitfalls associated with improper experimental methodologies and inadequate or faulty computational analysis. In particular, the use of measurements of hybridization signal intensities to infer gene expression involves many steps, some of which are poorly understood. Key to the design, analysis, and interpretation of microarray experiments is the understanding that the parameter being measured, signal intensity of indirectly labeled probes, is many steps removed from the parameter being inferred, gene expression. A typical microarray experiment represents a large-scale physiological study in which cells are isolated, RNA is harvested, and labeled representations of the harvested RNA are prepared and then used in hybridization experiments to indirectly label the nucleic acid probes constituting the array. The signal intensity of the label at each probe on the array is taken as a measure of gene expression for the genes specified by the probes on the array. It is also important to realize that the inferred gene expression is not that of a single cell, but rather that of a population of cells. In some cases, the population of cells under investigation is composed of many different cell types, each of which may have varied expression profiles unique to themselves. Microarray experiments are sensitive, albeit indirect, assays capable of measuring the genomic response to subtle changes in the environment that occur during the RNA harvesting process. Uncontrolled experimental variables may be introduced at any step in the wet laboratory work-up of microarray experiments, and may add to observed variances from array to array. In the design of microarray experiments, it is important to recognize areas where uncontrolled experimental variables may be introduced, so that they may be guarded against (although, in some cases, they are unavoidable). In these instances, variations in the experimental protocol should be documented (Brazma et al., 2001). Potential sources of uncontrolled experimental variables vary with individual applications. In clinical studies involving humans, the greatest potential for uncontrolled experimental variables exists. For instance, in clinical studies, uncontrolled experimental variables may include: 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 method of RNA isolation. In contrast, in experiments with tissue or cell cultures, passage number also needs to be added to the list of potential sources of variation mentioned above. In addition to apparent gene expression differences associated with uncontrolled experimental variables, biases and artifacts may be introduced by virtue of the methods used at each step of the procedure, including cellular and tissue harvest, RNA isolation, and labeling methods. Differential recovery of specific cell types from tissue may bias the gene expression profile observed for a particular tissue type. Likewise, RNA isolation protocols may introduce bias if they differentially recover membrane-bound RNA vs. soluble RNA. In one large study involving gene expression profiling of human leukocytes before and after Staphylococcus aureus enterotoxin B (SEB) treatment, the largest response variable was method of RNA isolation, and not SEB stimulation, although with both RNA isolation protocols, gene expression differences due to SEB stimulation were readily apparent (Feezor et al., 2004). Labeling reactions involving limited amplifications of the target material, such as those used with Affymetrix GeneChips (Affymetrix Inc., Santa Clara, CA, USA), can result in skewing if unequal amplification occurs during the in vitro transcription reactions. The last step in the indirect labeling of the array is the hybridization of the targets to the probes. The hybridization reaction is governed, in large part, by the specific sequences of the individual targets and probes and is affected by the ability of the target to form secondary structures with itself and with other molecules that may be present in the hybridization mixture. Target molecules that form extensive secondary structure with themselves tend to produce dimmer signals than targets that are devoid of secondary structure (Mir and Southern, 1999; Southern et al., 1999). Some hybridization protocols use a target fragmentation step in an attempt to circumvent secondary structure problems. Other factors that may affect hybridization from experiment to experiment, and hence hybridization signal intensity, are temperature and duration of hybridization, both of which are important experimental parameters that should be highly controlled. Microarray experiments by their nature are very complex experiments that indirectly provide a measure of gene expression. The steps between gene expression at the level of mRNA expression and measurement of the signal intensities of the probes arrayed are numerous, and some are poorly understood. Yet, with care, it is possible to use microarrays as a tool to begin to discern the dynamic changes that occur within cells as they respond to their environment, but great precautions must be taken to avoid contaminating the dataset with noise resulting from uncontrolled experimental variables. Microarray experiments are no different from any other experiment: For meaningful results, experiments must be replicated. The question that thus arises is not whether to replicate, but the number of replicates to perform and the level at which to replicate. Differences in gene expression due to uncontrolled experimental differences tend to dampen, while differences due to the controlled response variable tend to reinforce with replication. The number of replicates to perform is dependent, in part, on the noise associated with the system under study. For the simplest of experiments, such as those aimed at identifying differences between 2 cell lines, a minimum of 3 replicates per condition should be budgeted. Four replicates per condition are better and more appropriate if one is considering cross-validation methods, such as leave-one-out cross-validation or other statistical validation measures. The level of replication is dictated in part by the question being addressed. If the aim is to determine the variances in hybridization signal intensities associated with length of hybridization time, then technical replicates would be appropriate. Hybridization time would be the sole variable, and the experiment would be designed whereby one preparation of labeled target was repeatedly hybridized to different arrays for various times. If, however, gene expression differences are the goal, then the level of replication should be at the biological level, and the replicates should be independent. With clinical specimens, more replicates are usually required than for laboratory studies utilizing cell lines or isogenic strains, due to the higher coefficients of variation in hybridization signal intensities usually encountered with clinical material. The development of analytical methods for use on datasets derived from microarray expression studies is a rapidly changing and progressing field (Golub et al., 1999; Li and Wong, 2001; Tusher et al., 2001; Wright and Simon, 2003; Storey et al., 2005, 2007; Leek et al., 2006; Zhang, 2006). Most investigators utilize a combination of supervised and unsupervised methods in their analysis, and the individual methods of analysis used are somewhat of an art form that varies from investigator to investigator. However, several generally recognized methods of analysis are becoming acceptable. Most microarray analyses use a combination of supervised and unsupervised statistical methods. Unsupervised statistics attempt to define a model to fit observations, and are distinguished from supervised learning by the fact that there is no a priori output. In other words, in unsupervised statistics, input objects are treated as random variables. Thus, the first level of microarray data analysis is usually supervised. One simply seeks to determine which genes are most affected by a particular condition or treatment protocol. In this line of endeavor, the investigator makes use of the class labels of the samples—for example, wild-type vs. mutant—to determine the probes that display differential signal intensities. Early studies tended to rely on fold-change differences, and not on the use of statistics. In several studies published early in the microarray era, it was not even clear that replicates were performed. Among reports that utilize p-values or estimates of error based on permutations of the dataset in setting significance levels, the cutoff levels used remain largely arbitrary. In some cases, p-values as low as p = 0.05 for arrays with greater than 54,000 probes have been used. With such a modest threshold, 2700 probes would be expected to exceed the threshold by chance alone. Clearly, for larger arrays, a Bonferroni correction applied to the traditional p-value of 0.05 may be too stringent. A significance threshold of p < 0.001 has been offered as a compromise between the traditional p < 0.05 and a Bonferroni-corrected p < 0.05. Many investigators prefer to tune the significance level used for specific studies by estimating false discovery rates based on permutations of the dataset (Tusher et al., 2001). In many cases supervised analyses are used for purposes of identifying genes that can be used for class prediction—for example, to diagnose and differentiate diseased from normal tissue. In this case, the goal of the investigator is to identify probes that are predictors of the class labels, which then can be used in future studies to identify the nature of the specimen as normal or diseased, according to one or more of several prediction models. However, investigators should be aware that microarray experiments exemplify the "small n, large p" trap. The number of probes on a typical microarray, tens of thousands, vastly exceeds the number of categories into which the arrays can be classified. Thus, by chance alone it is likely that many probes can be identified out of a typical dataset that can distinguish between the small numbers of class labels in a typical study. Cross-validation studies and Monte Carlo simulations should be used to gauge the significance of the probes identified as predictors. Supervised analyses are only as good as the supervision applied. In cases where the class labels of the specimens are definitively known, as in comparing gene expression of a wild-type tissue vs. tissue of a knock-out organism where the genotypes of the wild-type and knock-out are precisely known, supervised analyses can be very powerful. However, when phenotypic distinctions are subtle and class labels are known with less certainty, as is the case often encountered in the clinical setting, where highly skilled pathologists may disagree over the diagnosis of a tumor as a particular cancer or grade, supervised analysis methods are hampered by misclassification errors at the supervision stage. Unsupervised analysis methods—including hierarchical clustering, k-means clustering, and self-organizing maps—can be used as tools for class discovery in situations where standard methods of assigning class labels are incomplete or inadequate. In situations where class labels can be assigned with impunity, as in studies designed to identify gene expression differences between wild-type and knockout animals, unsupervised cluster analysis can be used as an assessment of overall reproducibility of measurements between replicates. Experimental replicates should cluster together according to the controlled response variable—in this case, wild-type with wild-type and knockout with knockout. If replicates do not cluster together, or if clustering occurs according to some other identifiable variable, such as date of tissue harvest or date of labeled target preparation, then responses to uncontrolled experimental variables are likely contaminating the dataset, obscuring gene expression changes resulting from the controlled experimental variable.
For the experiments reviewed in this paper, we used array-to-array comparisons that were carried out by unsupervised and supervised methods, to assess the relatedness of the specimens under investigation using methods exhaustively described elsewhere (Handfield et al., 2005). Hierarchical clustering was first used to perform an unsupervised analysis. The resulting dendrogram revealed that the array chips from each infection state clustered together (Fig. 1
To characterize epithelial cell responses to species of differing pathogenic potential, and to assess the extent to which host responses are characterized by the challenging organism, we used transcriptional profiling to monitor the relative abundance of HIGK transcripts following co-culture for 2 hrs with A. actinomycetemcomitans, P. gingivalis, F. nucleatum, or S. gordonii. This was done by re-normalization of a collection of previously reported array experiments (methods presented in APPENDIX; from data presented in Handfield et al., 2005 and Hasegawa et al., 2007). Supervised analyses were next performed to identify gene expression differences between uninfected HIGK cells and infected cells in the 4 bacterial species (Fig. 1  ). Overall, the common core transcriptional response of epithelial cells to all organisms was very limited, and organism-specific responses predominated. No obvious correlation was found between the pathogenic potential or invasive potential of individual bacterial species and the core response of challenged host cells. To test the predictive validity of the probe sets identified, a "leave-one-out" cross-validation was performed with 4 different prediction models (linear discriminant, 1KNN, 3KNN, and nearest centroid; see APPENDIX Table). This validation step addressed the reproducibility of the datasets and the performance of the classifiers for each bacterial species.
It is a daunting challenge to analyze the massive amount of data that is generated by any microarray experiment, and to synthesize information that has biological relevance. One avenue is to apply gene ontology tools to analyze the complex array output and cull down to biologically significant information. Gene ontology is defined as a hierarchical structuring of the constantly evolving sum of knowledge that is compiled for all known genes and the pathways to which they relate. The structure is performed by subcategorizing genes according to their essential (or at least relevant) biological function. Thus, gene ontology can be applied to all organisms, even as knowledge of gene and protein roles in cells is accumulating and changing. Gene ontology uses statistical algorithms to structure and mine complex array data, which is then used to determine how biologically relevant information can be extracted from microarray data. Essentially, an ontology analysis compares the total number of genes of a given ontology family that are found consistently modulated in a given array experiment, with the total number of genes comprising the same pathway. This comparison can be quantified and used to determine the probability that this particular pathway is significantly affected, as compared with what would be expected if the number of genes found in an experiment were to be randomly distributed among all known pathways. It should be remembered that cells in vivo are subject to inputs from multiple signaling pathways, and these signaling pathways do not work in isolation (Jordan et al., 2000; Mehra and Wrana, 2002). Signaling cascades may intersect, or their activities may depend on the output of other simultaneous signals. Ontology analysis overlaid on the transcriptional profiles of HIGK cells upon infection with oral bacteria revealed that the most affected pathways were common among all bacterial infections (Table 1  ). Yet, for most affected pathways, each bacterium induced a characteristic pattern of expression in the host epithelial cells. In other words, the common core transcriptional response relates to the identity of the genes that, although different, were found still to affect similar pathways. Hence, although the transcriptional signature is different and characteristic of species, the host’s response is apparently limited to a few discrete pathways. Note that while 2 distinct organisms can affect the same pathway, they do not necessarily affect the same pathway in a consistent, physiologically relevant, and similar response.
The following paragraphs further dissect these differentially affected pathways in an attempt to provide additional insight into the intricate mechanisms that different microbes have evolved to manipulate epithelial cells in the oral cavity. (Because of space constraints, pathways of particular interest that are listed in Table 1 are depicted only in the APPENDIX. These include the cell cycle, the TOLL-like receptors, JAK-STAT signaling and cytokine profiles, TGF-β signaling, Wnt signaling, apoptosis, and MAPK signaling.)
The most common target of invasive bacteria in eukaryotic epithelial cells is arguably the cytoskeleton (Stebbins, 2004). The interactions between invasive bacteria and the cytoskeleton are numerous and of considerable complexity, reflecting the complexity of the cytoskeleton itself (Steele-Mortimer et al., 2000). Since cytoskeletal dynamics are central to immune function, cell shape and motility, organelle and chromosome movement, and phagocytosis, processes that can also be important to invading bacteria, evolution has favored species that have acquired the ability to modulate this important aspect of host cell biology (Steele-Mortimer et al., 2000; Stebbins, 2004). Cytoskeletal modulation can occur at several points of contact between bacteria and the host cells, and involves extracellular receptors, intracellular signal transduction, and cytoskeletal proteins themselves (Stebbins, 2004). Several examples, mostly taken from enteric pathogens, illustrate that various host cytoskeletal components are common targets of bacterial virulence determinants. These host cytoskeleton proteins include actin, tubulin, vimentin, profilin, filamin, fimbrin/plastin, and others (Steele-Mortimer et al., 2000; Gruenheid and Finlay, 2003; Pizarro-Cerdá et al., 2004; Stebbins, 2004). The eukaryotic proteins of the Rho family, such as Rho itself, Rac, and Cdc42, are key GTPases that exert signaling events ultimately leading to cytoskeletal organization of the cell (Steele-Mortimer et al., 2000; Stebbins, 2004). Such cytoskeletal changes are required for the formation of lamellipodia (membrane ruffles), filopodia (microspikes), stress fibers, and focal adhesions (Steele-Mortimer et al., 2000). Rho proteins also interface in the signaling cascades that regulate several other cellular processes, including endocytosis, secretion, cell-cycle regulation, and apoptosis (Steele-Mortimer et al., 2000).
While information regarding cytoskeletal responses to invasive enteric pathogens is well-established, less is known about the impact of oral bacteria on cytoskeletal structure. Infection of HIGK cells with all microbes tested reveals several significant differences in cytoskeletal pathway responses (Fig. 2
Many intracellular pathogens have independently evolved mechanisms to harness the activity of the actin cytoskeleton at different points, in some cases resulting in the formation of an actin tail that propels intracellular organisms between host cells. Strikingly, these strategies all converge on the Arp2/3 complex. This is a seven-protein complex that, when activated, nucleates de novo actin polymerization on the surface of the bacterium (Welch et al., 1997, 1998; Hueck, 1998; Zhou and Galan, 2001). Arp2/3 is activated by the Wiskott-Aldrich syndrome protein (WASP) family. These proteins, which serve a scaffolding function to bring together actin monomers and Arp2/3 to form a nucleation core, dictate the rate-limiting step in actin polymerization (Gruenheid and Finlay, 2003). It is interesting to note that Arp2/3 was induced by all bacterial species except P. gingivalis, although S. gordonii is not invasive, and A. actinomycetemcomitans is not thought to use actin for intracellular cell mobility (Meyer et al., 1999).
Surface receptors provide a means for bacteria to induce intracellular signals that affect the cytoskeleton (Cossart and Lecuit, 1998; Kahn et al., 2002; Stebbins, 2004). It has been established that P. gingivalis fimbriae bind and activate the β1-integrin receptor, and consequently induce signal transduction through downstream targets of the integrin receptor, such as Pyk2, Src, Rac,Arp2/3, FAK, and CAS, leading to actin and tubulin re-arrangements and bacterial uptake (Yilmaz et al., 2002). Integrins (ITG) were transcriptionally regulated following a pattern that was species-specific (APPENDIX Fig. 1.9). In particular, P. gingivalis up-regulated all integrins detected, in sharp contrast to F. nucleatum, which down-regulated all integrins detected. In the middle of this spectrum, S. gordonii down-regulated all integrins except
In addition, A. actinomycetemcomitans infection was characterized by the up-regulation of glycoproteins CD44, SV2,
In P. gingivalis-infected epithelial cells, actin remodeling has been shown to be required for P. gingivalis entry into gingival epithelial cells (Lamont et al., 1995; Abreu et al., 2001) and is known to be mediated by the engagement of integrins (Yilmaz et al., 2002). Prolonged invasion with intracellular P. gingivalis results in a cortical redistribution and condensation of actin microfilaments (Hasegawa et al., 2007). The impact of P. gingivalis on actin cytoskeletal architecture remodeling was associated here with the differential regulation of several actin binding proteins, including ACTN, WAVE2, Mena, mDia, and LIMK (Fig. 1.1). ACTN ( The focal adhesion pathway is closely interlaced with regulation of the actin cytoskeleton pathway. These pathways share several effector proteins that feed into one another. In particular, the integrins discussed above are critical molecules that mediate epithelial barrier formation, as well as cell activation, proliferation, differentiation, metabolism, and motility (Gumbiner, 1996; Nakagawa et al., 2006). Integrins provide a physical link, via focal adhesion, between the extracellular environment and the intracellular cytoskeleton (Clark et al., 1998). Focal adhesions are involved in cellular anchorage and directed migration, as well as in signal transduction pathways, which ultimately control wound healing and regeneration, along with tissue integrity (Hakkinen et al., 2000). Key factors in these events include paxillin and the focal adhesion kinase FAK. The phosphorylation of FAK is a central regulator of cell migration during integrin-mediated control of cell behavior (Schlaepfer et al., 1999). Paxillin is localized primarily at sites of adhesion of cells to the extracellular matrix (i.e., focal adhesions), and activation of this molecule is a prominent event upon integrin activation for actin-cytoskeleton formation, as well as the recruitment of FAK to robust focal adhesions (Ren et al., 1999; Nakamura et al., 2000; Nakagawa et al., 2006). Focal adhesion components were differentially regulated among the bacteria tested (APPENDIX Fig. 1.2). Although some extracellular matrix proteins (ECM, i.e., laminins) were up-regulated by all bacterial species, only A. actinomycetemcomitans and P. gingivalis also up-regulated their corresponding receptors, the alpha- and beta-integrins (ITGA and ITGB, respectively). Furthermore, epidermal growth factor (EGF) and its receptor (RTK) were up-regulated by A. actinomycetemcomitans and P. gingivalis, while F. nucleatum induced EGF but not its receptor. FAK and paxillin were not transcriptionally modulated by S. gordonii, F. nucleatum, or P. gingivalis, and both components were down-regulated by A. actinomycetemcomitans. It has also been demonstrated that GECs infected with P. gingivalis demonstrate a significant redistribution of paxillin and FAK from the cytosol to cell peripheries and assembly into focal adhesion complexes, which are dependent on the expression of FimA. Ultimately, the majority of paxillin and FAK returns to the cytoplasm, with significant co-localization with P. gingivalis in the perinuclear region (Yilmaz et al., 2002, 2003). Interestingly, enhanced FAK immunostaining is detected in small populations of pre-invasive (carcinoma in situ) oral cancers and in large populations of cells in invasive oral cancers. FAK is probably not a classic oncogene, but has been suggested to be involved in the progression of cancer to invasion and metastasis (Kornberg, 1998; Kornberg et al., 2005), which may offer some physiological basis that provides a mechanistic understanding for a possible link between infection with oral pathogens and oral cancers. Seemingly, some pathogens bind and modify junction components directly, while others exert their effects through the actin cytoskeleton, which ultimately controls the integrity of tight junctions (Gruenheid and Finlay, 2003). Remarkably, P. gingivalis displays a dual action, highlighting again the relevance of the manipulation of the host cytoskeleton in oral host-microbe interactions. In this regard, P. gingivalis binds to and/or degrades gingival proteoglycans and matrix proteins, including laminin and fibronectin (Ellen, 1999), as well as directly affecting the cytoskeleton through secreted products, such as SerB. Although much work is being currently done to identify the host and other bacterial players involved in these phenomena, the exact sequence of events and their interrelation remains to be established. Considering the co-evolution of oral commensals and the epithelial surface of the oral cavity, and the profound effect that S. gordonii has on the transcriptome of epithelial cells, it may be more appropriate to assume that the normal physiologic steady-state of epithelial cells is in continuous response to commensal bacterial species. Altogether, one could argue that an infected state is normal, and possibly beneficial for the oral epithelium, since it confers a state of wound healing, driven by infection or co-existence (Ellen, 1999). Oral pathogenicity would then be associated with the expression of virulence determinants that impinge on the cytoskeleton, possibly reflective of failure to reach evolutionary balance. Dysregulation of cytoskeletal proteins may have a profound effect on the normal physiologic homeostasis of the periodontium, affect the normal physiologic remodeling of the tissue, and exacerbate inflammatory pathways.
Eukaryotic cells coordinate their cell division through 4 phases: cell growth and preparation for replication (G1, or gap1 phase), chromosome duplication (S, or synthesis phase), growth and preparation for mitosis (G2, or gap2 phase), and mitosis (M phase). This cell cycle is orchestrated by a set of protein kinases that initiate the successive stages of each cycle and that are associated with regulatory protein subunits called cyclins. To regulate cell cycling, levels of cyclin-dependent kinases (Cdks) are modulated. The kinase activity of Cdks is regulated by association, attachment, binding of inhibitors, phosphorylation and dephosphorylation, and degradation of the associated cyclin. These cyclins ultimately phosphorylate downstream substrates and mediate various cellular processes during cycling (Elledge, 1996; Kaufmann and Paules, 1996; Rotman and Shiloh, 1999; Smith and Bayles, 2006).
Infection with all organisms tested had profound and diametrically opposed effects on cell cycling of gingival cells (APPENDIX Fig. 1.3). For example, the cell division cycle proteins CDC20 and CDC25B mediate mitotic progression and are highly expressed in proliferating cells, their levels peaking in the M phase. Both CDC20 and CDC25B were down-regulated by A. actinomycetemcomitans and P. gingivalis, but up-regulated by F. nucleatum and S. gordonii. Consistent with this effect, Bub1 and Bub3, involved in cell cycle checkpoint enforcement, were also down-regulated by A. actinomycetemcomitans and P. gingivalis and up-regulated by F. nucleatum and S. gordonii. Only 2 genes were consistently modulated upon infection: GADD45 was up-regulated, whereas Cyclin E (CycE) was down-regulated by all 4 micro-organisms. The growth arrest and DNA-damage-inducible GADD45, as the name indicates, was originally identified as a gene that is induced by agents that cause DNA damage (Fornace et al., 1988; Papathanasiou et al., 1991; Hasegawa et al., 2007). Transcriptional regulation of the GADD45 gene is mediated by both p53-dependent and -independent mechanisms (Takekawa and Saito, 1998; Hasegawa et al., 2007), and GADD45 family members ( In sharp contrast, Cyclin A and Cyclin D were up-regulated by S. gordonii, non-regulated by F. nucleatum, and down-regulated by both P. gingivalis and A. actinomycetemcomitans. Cyclin A1 (also known as CCNA1) complexes CDK2, and is overexpressed in the early G1 phase to reach highest levels during the S and G2/M phases, whereas Cyclin A2 (CCNA2) accumulates steadily during G2 and is abruptly destroyed at mitosis (GeneCards). In addition, both A. actinomycetemcomitans and P. gingivalis down-regulated Cyclin B, which is essential for the control of the cell cycle at the G2/M transition (GeneCards), and down-regulated CDK1 (also known as CDC2 and p34 protein kinase), which plays a key role in the control of the eukaryotic cell cycle during entry into the S phase and mitosis. CDK1 is activated by CDC25 and continually shuttles between the nucleus and cytoplasm. CDK1 is maintained in an inactive state through phosphorylation by WEE1 (also up-regulated in A. actinomycetemcomitans-infected cells) and MYT1. CDK1 is thought to be up-regulated by c-Myc, another gene that is down-regulated by all organisms, except P. gingivalis. In A. actinomycetemcomitans-and F. nucleatum-infected cells, Kip1 and Kip2 (also known as cyclin-dependent kinase inhibitor CDKN1B or p27, and CDKN1C or p57) were up-regulated, providing an additional level of repression for Cyclin-A, -D, and -E. Kip1,2 are involved in TGFβ-induced G1 arrest, and in the cellular response after DNA damage. In the case of A. actinomycetemcomitans-infected cells, this was also consistent with the induction of ATM and DNA-PK (also known as PRKDC), which have been shown to be central to the genotoxic effect of the cytolethal distending toxin (CDT) of A. actinomycetemcomitans (Alaoui et al., unpublished observations). Several checkpoints can stop the cell cycle, in response to incomplete replication or damaged DNA, by delaying the progression of the cycle until the DNA damage is repaired, which ensures that the essential events of a cell-cycle stage are completed before progression to the next stage. Phenotypically, the checkpoints extend the length of a stage for DNA repair to take place prior to DNA replication and mitosis (Elledge, 1996; Kaufmann and Paules, 1996). A. actinomycetemcomitans was the only organism able to up-regulate Cyclin H, while P. gingivalis was the only organism that induced CDK7. Cyclin H (also known as CCNH) regulates CDK7, the catalytic subunit of the CDK-activating kinase (CAK) enzymatic complex. CAK activates the cyclin-associated kinases CDC2/CDK1, CDK2, CDK4, and CDK6 by threonine phosphorylation, while CAK is involved in cell-cycle control and in RNA transcription by RNA polymerase II. Interestingly, the expression and activity of Cyclin H have been thought to remain constitutive throughout the cell cycle (GeneCard). This may be another example where the more pathogenic organisms have developed strategies to manipulate the cell cycle. Alternatively, this may constitute a feedback loop aimed at maintaining homeostasis by up-regulating the cyclins that are apparently down-regulated by both A. actinomycetemcomitans and P. gingivalis. Overall, at 2 hrs of infection, the differential modulation of all cyclins, as well as the cell division cycle proteins, would argue that S. gordonii and, to a lesser extent, F. nucleatum can stimulate the transition through the G1/S and the G2/M phases, as compared with uninfected control cells. In contrast, A. actinomycetemcomitans appeared to delay cell cycle progression, likely in response to genotoxic stresses induced by the cytolethal distending toxin (CDT).
Gingival epithelial cells, as the first physical line of defense against the oral microflora, locally orchestrate the immune reaction through the specific recognition of pathogen-associated molecular patterns (PAMP) by their respective TOLL-like receptors (TLRs) (Akira and Takeda, 2004). For example, TLR ligands include bacterial products such as lipoproteins, glycolipids and peptidoglicans (TLR2), lipopolysaccharide (TLR4), flagellin (TLR5), and bacterial DNA (TLR9). TLR2, TLR4, and other TLRs are expressed in numerous oral epithelial cells (primary and transformed). However, TLR-2 and -4 remain the only 2 that have been detected in gingival tissue from periodontitis patients to date (Asai et al., 2001, 2003; Yoshimura et al., 2002; Mori et al., 2003; Kusumoto et al., 2004; Ren et al., 2005; Brozovic et al., 2006; Milward et al., 2007). These 2 TLRs have been shown, in certain instances, to be transcriptionally modulated by challenges with P. gingivalis and F. nucleatum (Brozovic et al., 2006; Milward et al., 2007). Genetically, the polymorphism of TLR2 and TLR4 that has been observed in persons with periodontitis has been suggested to contribute to an increased susceptibility to this disease (Folwaczny et al., 2004; Laine et al., 2005; Schroder et al., 2005; Kinane et al., 2006; Milward et al., 2007). TLR2 and TLR4 were not transcriptionally modulated by any of the species tested (at 2 hrs of co-culture with live organisms) (APPENDIX Fig. 1.4). However, downstream events associated with TLR signaling were clearly modulated, supporting the significant role of this pathway in host-microbe interactions. In fact, signaling through TLR2 and TLR4 was evident for all bacterial species tested. One of the central components of the response to cytokine induction via the TOLL-like receptor signaling pathways is the JAK (Janus Kinase)/STAT (signal transducer and activator of transcription) pathway (APPENDIX Fig. 1.5). JAKs are associated with intracellular domains of several cytokine membrane receptors, and can activate members of the STAT family by phosphorelay. STAT thus activated translocates to the nucleus to modulate specific transcriptional responses (Schindler, 2002; Planas et al., 2006). The JAK/STAT signaling pathway is involved in several cellular pathways, such as cell proliferation, cell cycle, apoptosis, and regulation of the immune response (O’Shea et al., 2004; Planas et al., 2006). These pathways cross-talk, exert feedback loops, and affect each other at the transcriptional level. The molecular mechanisms underlying the regulation of JAK/STAT activity are very complex and still not fully understood. There is no doubt, however, that such activity plays a major role in the regulation of inflammatory and immune responses to cytokines in response to infection (Planas et al., 2006). Surprisingly, there is little information available on how oral bacteria modulate and/or may impinge on the JAK/STAT signal transduction pathway in the oral cavity, and how this can affect the maintenance of health and disease progression (Mao et al., 2007).
Infection of HIGK cells with all microbes tested invariably modulated several cytokines and their cellular surface receptors (APPENDIX Fig. 1.4). However, the cytokine profiles varied considerably and characterized each challenging organism. S. gordonii up-regulated interleukin IL8 and IL23
Numerous cytokines are located upstream of JAKs, which were transcriptionally induced by HIGK cells by S. gordonii and A. actinomycetemcomitans (JAK1) only. In contrast, STATs (STAT1 and STAT5B) were induced by both A. actinomycetemcomitans and P. gingivalis, but were down-regulated by F. nucleatum and S. gordonii (STAT1, STAT2 in S. gordonii only, and STAT3 in both organisms). Since the action of signaling by STATS may be very transient (Wormald and Hilton, 2004; Planas et al., 2006), it is recognized that a transcriptional "snapshot" at a single time-point may provide only a limited insight into long-term biological properties. However, the stringency of the statistical methods applied in this model system confers great confidence that the JAK/STAT pathway is indeed modulated upon infection, and apparently differs between infecting species, which warrants further dissection. For example, a recent report confirmed that P. gingivalis can block apoptotic pathways in primary gingival epithelial cells through the manipulation of the JAK/STAT pathway, ultimately modulating the intrinsic mitochondrial cell death pathways as a means of intracellular survival. By quantitative real-time reverse-transcription polymerase chain-reaction, expression of STAT3 was shown to be elevated in P. gingivalis-infected cells. Also, Western analysis confirmed that the levels of phosphorylation of JAK1 and Stat3 increased upon infection (Mao et al., 2007). The ras and MAPK pathways can be stimulated, in response to a trigger of JAK, to modulate cell cycle and apoptosis further. This branch of the pathway was induced by both A. actinomycetemcomitans and P. gingivalis, and was consistently repressed by F. nucleatum and S. gordonii. Another branch downstream of the JAK sensing system is the PI3K/AKT pathway, which is an important modulator of apoptosis. AKT was found to be consistently up-regulated by both A. actinomycetemcomitans and P. gingivalis, relative to the level of expression found in F. nucleatum and S. gordonii. Altogether, the pattern presented above suggests that more pathogenic organisms have evolved mechanisms to impinge directly on MAPKs, thus reprogramming the cell to remain viable despite infection.
Conceivably, infected gingival epithelial cells can either attempt to eliminate challenging micro-organisms, tolerate the spread of infection and invasion, or simply induce cell death to protect neighboring cells from infection (Menaker and Jones, 2003). In any of these scenarios, the decisive determinant of cell behavior is the signaling system that is activated in the host cell upon infection (Finlay, 1997; Kyburz et al., 2003; Litchfield, 2003; Sancar et al., 2004; Uitto et al., 2005). Mitogen-activated protein kinase (MAPK)-related signal transduction pathways are among the most widespread mechanisms of eukaryotic cell regulation (activation, stress response, differentiation, and growth). MAPKs are activated following engagement of numerous cell-surface receptors, and the MAPK-dependent activation of transcription factors is considered to be a prerequisite for altered gene expression in stimulated target cells (for a review, see Chang and Karin, 2001; Kyriakis and Avruch, 2001; Walter et al., 2004). There are three well-characterized subfamilies of MAPKs: the extracellular signal-regulated kinases (ERK), the c-Jun NH2-terminal kinases (JNK), and the p38 family of kinases (p38 MAPKs) (Johnson and Lapadat, 2002; Hommes et al., 2003). ERK activation is considered essential for entry into the cell cycle and, thus, for mitogenesis. Activation of the JNK pathway is associated with programmed cell death or apoptosis. The p38 MAPKs regulate the expression of many cytokines and have an important role in activation of immune responses (Johnson and Lapadat, 2002; P Zhang et al., 2005). While the JNK and p38 pathways are activated by many pro-inflammatory cytokines and by environmental stress and lead to altered gene expression and apoptosis, the ERK MAPK pathway is induced by several growth factors and mitogens, and results in control of cell proliferation through stimulation of mitosis-associated protein kinases (Marshall, 1999). Considerable cross-talk exists between and among the different MAPK pathways. The MAP kinases also interact with intrinsic heat-shock proteins that are known to modulate cell behavior (Zhang et al., 2001). The activation of intracellular signaling pathways and subsequent inflammatory cytokine production has been induced by different stimuli in different cell types; however, the response induced by one stimulus cannot be extrapolated to another or from one cell type to another (Rao, 2001; P Zhang et al., 2005).
The transcriptional profiles obtained from infected HIGK cells were characterized by very little consistency among all 4 species tested (APPENDIX Fig. 1.9). Overall, F. nucleatum and S. gordonii appeared to perturb the MAPK signaling pathway’s transcriptome much less significantly than did either A. actinomycetemcomitans or P. gingivalis, providing additional evidence that less pathogenic species present a greater degree of host adaptation as compared with more pathogenic species. In particular, all 3 MAPK subfamilies (ERK, JNK, and p38) were transcriptionally up-regulated by A. actinomycetemcomitans. Several A. actinomycetemcomitans molecules are known to be sensed through multiple MAPK pathways. For instance, hsp60 from A. actinomycetemcomitans triggers the ERK1/2 MAPK pathway and is involved in hsp60-induced cell growth, which may, in case of mucosal infection, lead to increased wound repair (Zhang et al., 2001). In addition, hsp60 released by human structural or inflammatory cells may contribute to increased cell motility in inflamed tissue. In the case of tissue repair, this may mean accelerated wound closure. Furthermore, hsp60-induced epithelial cell migration may lead to local invasion of infected epithelium in some mucosal infections, or to increased cell invasion in infected tumors (Zhang et al., 2004a,b). Besides hsp60, the LPS from A. actinomycetemcomitans induces rapid p44 and p42 phosphorylation (ERK 1 and ERK 2, respectively) in human gingival fibroblasts (Gutierrez-Venegas et al., 2006) and activates ERK, JNK, p38, and I
In contrast to A. actinomycetemcomitans, P. gingivalis up-regulated JNK only transcriptionally. In primary oral gingival epithelial cells (GEC), previous work has confirmed that P. gingivalis can selectively target components of the MAP kinase pathways. In particular, ERK1/2, while not involved in P. gingivalis invasion of GECs, may be down-regulated by internalized P. gingivalis, while the activation of JNK is associated with the invasive process of P. gingivalis (Watanabe et al., 2001). Others have suggested that, in endothelial cells, P.gingivalis strains induced phosphorylation of p38 MAPK, degradation of I
It has previously been shown that NF- In the maintenance of oral health or during disease development, accumulating evidence supports the central role of MAPKs. Their significant differential regulation by all bacteria studied to date remains particularly compelling evidence that they are key to various (and diverse) responses to infection. Indeed, MAPK transduction is involved in maintaining the balance between cellular proliferation and cellular death, thus fine-tuning cellular turnover and directing wound healing and clearance of invading organisms. It remains to be investigated whether the transcriptional discrepancies noted above reflect the transient nature of MAPKs.
Transforming growth factor (TGF)-β is a multifunctional cytokine that is involved in various cellular functions, such as angiogenesis, immune suppression, extracellular matrix synthesis, apoptosis, and cell-growth inhibition (Postlethwaite et al., 1987; Wahl et al., 1987; Noda et al., 1988; Yamaji et al., 1995; Prime et al., 2004; Gurkan et al., 2006). Of particular interest in the context of host-microbiota interactions, TGF-β is one of the key cytokines with pleiotrophic properties that has both proinflammatory and anti-inflammatory features in regulation of the inflammatory infiltrate and in resolution of inflammation (Wahl and Chen, 2005; Gurkan et al., 2006). Furthermore, TGF-β affects cell proliferation and the differentiation process, making it an important cytokine in wound healing, tissue remodeling, and regeneration (Sporn and Roberts, 1993; Gurkan et al., 2006), and in enhancing epithelial barrier functions (Howe et al., 2005). TGF-β has a central role in the regulation of collagen metabolism in physiologic as well as pathologic conditions, like periodontitis (van der Zee et al., 1997; Gurkan et al., 2006). Moreover, reduced TGF-β levels at a wound area may lead to impairment in healing (Gurkan et al., 2006). Further, it is likely that the coupling of bone formation and bone resorption is mediated by local factors in the bone microenvironment. TGF-β acts as a regulatory growth factor for osteoblasts, and it has been suggested that it affects their functions (Pfeilschifter et al., 1987, 1988; Wahl et al., 1988; Yamaji et al., 1995). It has also been suggested that, following stimulation with LPS, TGF-β accumulates in inflammatory lesions and suppresses immune cell function, but does not lead to tissue destruction (Yamaji et al., 1995). Compared with samples from healthy individuals, increased TGF-β levels are found in gingival tissues and gingival crevicular fluid (GCF) samples from persons with gingivitis, chronic periodontitis, generalized aggressive periodontitis, and peri-implantitis (Steinsvoll et al., 1999; Buduneli et al., 2001; Cornelini et al., 2003; Ejeil et al., 2003; Wright et al., 2003; Gurkan et al., 2006). Genetic polymorphisms in the TGF-β gene have been shown to interfere with the production, secretion, or activity of this growth factor (Awad et al., 1998; Li et al., 1999; Atilla et al., 2006), and this has been associated with risk for systemic diseases, including cardiovascular diseases and rheumatoid arthritis, which are related to periodontitis in terms of chronic inflammatory processes (Garcia et al., 2001; Sugiura et al., 2002; Mercado et al., 2003; Atilla et al., 2006). Epithelial surfaces up-regulate TGF-β in response to infection with other non-oral bacterial pathogens, including Yersinia, Cryptosporidium, EHEC O157:H7, and EPEC (Howe et al., 2005). Collectively, this response may reflect the host’s attempt to restore cell integrity or to avoid cell destruction upon microbial challenge (Bohn et al., 2004; Howe et al., 2005). Signaling through TGF-β includes the activation of ERK, MAPK, and SMAD signaling, and affects a plethora of downstream functions (APPENDIX Fig. 1.6). Infection of HIGK cells with all microbes tested significantly modulated several components of the TGF-β signaling pathway. Notably, the pattern of expression presented striking differences related to more/less overt pathogenicity. Most genes were down-regulated in S. gordonii- and F. nucleatum-infected HIGK cells. In contrast, A. actinomycetemcomitans and P. gingivalis consistently up-regulated most genes affected. The most significant differences were in TGF-β itself (down-regulated by P. gingivalis) and in the level of expression of Smad1/5/8, which was down-regulated by A. actinomycetemcomitans, but up-regulated by P. gingivalis. In sum, this response appeared to correlate the inflammatory potential of oral pathogenic species, and may reflect the host’s attempt to restore cell integrity or to avoid cell destruction upon microbial challenges with overt pathogens, as previously suggested in respiratory and gastrointestinal systems (Bohn et al., 2004; Howe et al., 2005).
The Wnt gene family is a group of highly conserved developmental genes involved in cell growth regulation, differentiation, and organogenesis. Beta-catenin, a central component of this pathway, also links cell adhesion and cell differentiation; it stabilizes cell-cell adhesion by anchoring cadherins via  -catenin to the cytoskeleton (Kemler, 1993; Takeichi, 1995; Gradl et al., 1999). Wnt pathway signaling is mediated via interactions between β-catenin and members of the LEF/TCF family of transcription factors (Behrens et al., 1996; Huber et al., 1996; Molenaar et al., 1996; Gradl et al., 1999; Lo Muzio et al., 2002). The Wnt/Wg signaling cascade includes a membrane-integrated receptor of the frizzled family, which activates the phosphoprotein dishevelled (Dsh, also known as Dvl), leading to the inhibition of glycogen synthase kinase 3b. Because β-catenin is a substrate of this serine/threonine kinase, it remains hypophosphorylated upon Wnt signaling and accumulates in the cytoplasm. This promotes β-catenin binding to LEF-TCF transcription factors. The β-catenin-LEF-TCF heterodimer enters the nucleus and is able to activate or repress gene transcription (for detailed reviews, see Gumbiner, 1995; Cavallo et al., 1997; Kuhl and Wedlich, 1997; Gradl et al., 1999). The genes from the Wnt pathway are also involved in oncogenesis. Indeed, β-catenin has been reported to be involved in the genesis of numerous human cancers (Lo Muzio et al., 2002). Abnormally high concentrations of β-catenin have been reported in several tumor and carcinoma cell lines caused by mutations in the adenomatous polyposis coli (APC) gene or β-catenin gene. These mutations prevent the degradation of β-catenin (Munemitsu et al., 1995; Gradl et al., 1999), which then contributes to the formation of a constitutively active β-catenin-LEF-TCF transcription complex (Ilyas et al., 1997; Korinek et al., 1997; Morin et al., 1997; Rubinfeld et al., 1997; Sparks et al., 1998; Gradl et al., 1999; Sakanaka et al., 2000; WM Zhang et al., 2005). Most recently, the proto-oncogene c-myc was identified as a direct target gene of the β-catenin-Tcf-4 complex in a human colorectal cancer cell line. This links the up-regulation of β-catenin to loss of proliferation control in tumorigenesis (Gradl et al., 1999). Furthermore, mutations in β-catenin have been observed in several carcinoma and melanoma cell lines, revealing a constitutively active β-catenin-LEF-TCF complex (Korinek et al., 1997; Morin et al., 1997; Rubinfeld et al., 1997; Gradl et al., 1999). The Wnt and the TGF-β (described above) pathways are known to interact in a range of biological functions, suggesting a certain interdependence. For example, a TGF-β-dependent interaction between Smad3 and Lef1 has been demonstrated and shown to regulate synergistic induction of Wnt target genes (Korinek et al., 1997; Morin et al., 1997; Rubinfeld et al., 1997). It is unknown how extensive this interplay will turn out to be, but it may be a critical factor in governing the precise execution of complex developmental programs and may be important in the initiation or progression of human cancer (Mehra and Wrana, 2002). Infection of HIGK cells has been characterized by a transcriptional profile that presented very little consistency among all 4 species tested, although Wnt itself was induced by all bacteria except P. gingivalis (APPENDIX Fig. 1.7). Nonetheless, the gene expression pattern clustering around the β-catenin/TCF branch of this pathway was particularly affected by both A. actinomycetemcomitans and P. gingivalis. In particular, infection with A. actinomycetemcomitans consistently modulated a large proportion of the genes involved. Notably, β-catenin, APC, and TCF/LEF were up-regulated, leading to obvious implications in the regulation of downstream genes on the cell cycle that included c-myc, c-jun, and cycD (see above for a discussion on the cell-cycle pathway). We have previously raised the question of the in vivo consequences of P. gingivalis’ anti-apoptotic activity in oral cancer, where the ability of P. gingivalis to suppress apoptosis could provide a likely mechanism (Nakhjiri et al., 2001; Handfield et al., 2005; Mao et al., 2007). In these studies, the disruption of normal tissue homeostasis was predicted to occur, which would negatively affect wound healing in the periodontal lesion. Of potentially greater consequence, up-regulation of STATs was associated with infection of P. gingivalis, providing a likely underlying association with apoptosis, and possibly oncogenesis (Mao et al., 2007). While bacteria have traditionally been considered as bystanders in the development of cancers, the frequent targeting of cell-cycle and apoptotic pathways by various pathogenic bacteria has led to a re-appraisal of this status (Lax and Thomas, 2002; Nougayrède et al., 2005). In the best-documented example with Helicobacter pylori, analysis of epidemiological, molecular, and animal model data provides convincing evidence for a role in gastric cancer development (Kusters et al., 2006). With regard to oral bacteria, evidence of Streptococcus anginosus, S. mitis, and Treponema denticola DNA has been found in esophageal cancer tissue, and DNA from S. anginosus was recovered from head and neck squamous cell carcinoma (Tateda et al., 2000; Sasaki et al., 2005). High salivary counts of Capnocytophaga gingivalis, Prevotella melaninogenica, and S. mitis have been proposed as diagnostic indicators of oral squamous cell carcinoma (Mager et al., 2005). It is yet unclear if this specific interaction provides a sufficient framework to sustain the association of A. actinomycetemcomitans with certain forms of oral cancer. In light of the evidence presented above, it is tantalizing to speculate that the modulation of β-catenin/TCF observed as a result of infection of HIGK with both A. actinomycetemcomitans and P. gingivalis may provide an underlying mechanistic basis for the loss of proliferation control in tumorigenesis.
Apoptosis is a programmed form of cell death that results in the elimination of specific cells without disturbance of tissue structure or function (Kerr et al., 1972; Vaux et al., 1988; Cohen et al., 1992; Tonetti et al., 1998). In the oral cavity, apoptosis plays a pivotal role in a wide variety of biological phenomena, including development, differentiation, remodeling of inflamed tissue, homeostasis, and regulation of the inflammatory responses (Cohen et al., 1992; Haslett et al., 1994; Tonetti et al., 1998). In epithelial cells, the apoptotic process can be modulated by various factors, including hormones, cytokines, growth factors, and infection. Among other effectors of the apoptotic response, the products of p53 and Bcl-2 proteins have been shown to play fundamental roles (Vaux et al., 1988; Tonetti et al., 1998; Handfield et al., 2005; Mao et al., 2007). Emerging evidence supports the concept that bacterium-modulated apoptosis is a relevant phenomenon in the pathogenesis of periodontal diseases (Chen and Zychlinsky, 1994; Kato et al., 1995; Tonetti et al., 1998; Wang et al., 1999; Arakawa et al., 2000; Graves et al., 2000; Jarnbring et al., 2002; Bantel et al., 2005; Vitkov et al., 2005). In the normal oral mucosal surface, there is rapid renewal of epithelial cells, a response thought to facilitate exfoliation and clearance of infected cells (Gao and Kwaik, 2000b; Häcker et al., 2006; Mao et al., 2007). Apoptosis-related DNA damage and the expression of p53 and Bcl-2 are prevalent in clinically healthy gingival tissue exposed to chronic, low-grade, bacterial challenge and inflammation (Tonetti et al., 1998). Preservation of periodontal health is thus dependent on the establishment and the maintenance over time of local host-bacterium equilibrium (Genco, 1992; Chen and Zychlinsky, 1994; Tonetti et al., 1998; Jarnbring et al., 2002). Since the junctional epithelium is constantly exposed to a mixed bacterial flora, it is thought that the existence of a high rate of epithelial cell turnover and a highly regulated local immune response would contribute to bacterial clearance and would limit the invasion of bacteria into the gingival tissues (Page, 1991; Genco, 1992; Tonetti et al., 1998; Jarnbring et al., 2002; Smith and Bayles, 2006). According to one estimate, epithelial cell desquamation in this tissue is 50 to 100 times faster than in the adjacent oral mucosa (Listgarten, 1972). Microscopic evidence suggests that the deeper part of the pocket epithelium in persons with periodontitis patients presents an increased exfoliation of epithelial cells, a higher level of bacterial internalization, as well as internalization-induced epithelial apoptosis (Saglie et al., 1982a,b,c; Vitkov et al., 2005). Accumulating evidence from in vitro studies supports the concept that several intracellular bacteria can evoke elevated apoptosis in host epithelial cells. Cells with p53 expression and DNA damage are mainly localized in the epithelium and connective tissue of persons with periodontitis (Jarnbring et al., 2002; Bantel et al., 2005). Infection by certain oral organisms could thus contribute to pathogenesis by inhibiting both cellular and humoral immunity via apoptosis of immune response cells. A. actinomycetemcomitans or its extracellular products may directly induce apoptosis of host cells (Mangan et al., 1991; Zychlinsky et al., 1992; Tonetti et al., 1998). In fact, it has been proposed that the slow healing and chronic nature of untreated aggressive periodontitis may be associated with the dysregulation of normal apoptosis (Jarnbring et al., 2002; Smith and Bayles, 2006). Conversely, several bacterial pathogens, including Chlamydia, Neisseria, Salmonella, and Porphyromonas, can impinge on apoptotic pathways to extend host cell survival (Gao and Kwaik, 2000a; Nakhjiri et al., 2001; Häcker et al., 2006; Simons et al., 2006). Prevention of host epithelial cell death by intracellular bacterial pathogens may prolong the integrity of their intracellular environment, and thus favor bacterial persistence (Mao et al., 2007). The transcriptional profiles resulting from the infection of HIGK cells with the oral bacterial species tested are depicted in APPENDIX Fig. 1.8. Very little consistency was observed among the 4 species. Overall, F. nucleatum and S. gordonii perturbed the gingival epithelial cell transcriptome much less significantly than did P. gingivalis and A. actinomycetem comitans. This correlates with the ultrastructural and phenotypical studies performed on healthy and diseased tissue samples (discussed above). That the apoptosis pathway follows a strikingly similar trend to what has been previously described with the MAPK signaling pathway further emphasizes that there is a great degree of host adaptation by the less pathogenic species. It has previously been suggested that the transcriptional effect triggered by bacterial adhesion, invasion, or toxicity may lead to an aberrant epithelial apoptosis program, and thus contribute to the greater pathogenic potential of species that disproportionately modulate apoptosis-related genes (Handfield et al., 2005; Hasegawa et al., 2007). Further characterization of the apoptosis pathway confirmed that several differentially regulated gene products linked to the p53 apoptotic network for both A. actinomycetemcomitans and P. gingivalis, which ultimately correlated with the pro-apoptotic phenotype observed with A. actinomycetemcomitans and the anti-apoptotic phenotype of P. gingivalis observed on epithelial cells (Handfield et al., 2005).
DNA microarray technology has established itself as a major new research tool for the analysis of gene expression. The inference of gene expression from array data is indirect and involves many steps, each of which can become a source of noise if left uncontrolled. The nature and characteristics of microarray experiments present numerous challenges that must be overcome if high-quality datasets with low noise and high informational content are to be obtained. With appropriate experimental design, execution, and proper analytical and statistical methods, DNA microarray technology will likely take its place with the microscope as an invaluable tool in the biological sciences, providing a window through which to view the genome as it dynamically responds to changes in its intracellular and extracellular environment. In contrast to the current paradigm, infection of oral epithelial cells by microbial species with different pathogenic potentials differentially affected a select subset of host cells’ pathways, as measured by transcriptional profiling. It is conceptually likely that co-evolution of the distinct oral species with the oral mucosal surface resulted in a gradient of potential to manipulate epithelial cells. Although common pathways were differentially affected by all bacterial species, which probably reflects the similar evolutionary pressures that all micro-organisms experience in the oral ecological niche, the common core transcriptional responses of epithelial cells to more pathogenic species were very limited, and organism-specific responses predominated. Overall, F. nucleatum and S. gordonii perturbed the transcriptome of most pathways much less significantly than did A. actinomycetemcomitans or P. gingivalis, which supported the concept that less pathogenic species also present a greater degree of host adaptation, and tread more lightly on host cells, as compared with more pathogenic species. Obviously, transcriptional profiling provides only an incomplete view of cell signaling, and does not take into account post-transcriptional events. Although incomplete, it is irrefutable that the most affected pathways described herein are central to the host cells’ response to infection with oral microbiota. Because of the involvement of multiple signaling pathways and the crosstalk between multiple signaling modulators, careful studies on the biologic roles of the signaling pathways/modulators activated by infection will provide further understanding of host-microbe interactions in the oral cavity, including the immune responses. Ultimately, future studies will focus on increasingly complex experimental models, including consortia of organisms grown in biofilms, as well as interacting with human biological specimens obtained from both healthy patients and those with disease. This may naturally lead to a better understanding of the overall bacterial involvement in periodontal disease, help decipher the contributions of key bacterial virulence determinants that are specifically induced during disease, and more directly substantiate the role of single species in mixed-species disease. Future studies will need to address whether there is a beneficial protective effect associated with the less-disruptive bacteria in normal oral microflora to challenges with more disruptive species. Such antagonistic effects would support the concept that the importance of less-disruptive/commensal organisms goes beyond the occupation of the ecological niche in a mixed microbiota. Analysis of the data presented above would support the notion that commensals can also reprogram the epithelium to potentiate beneficial wound repair and remodeling. Further confirmation in primary cells and extension to clinical specimens will provide additional confidence in the clinical applicability and generalization of the lessons learned herein. Notwithstanding the caveats mentioned above, the studies performed to date provide insight into the intricate interactions occurring between specific bacterial species and host epithelial cells. Extension of these studies will further our fundamental understanding of the pathogenic mechanisms used by oral bacteria, the beneficial effects of commensals, and the means by which host cells identify and discriminate between organisms. It is anticipated that the results could form the basis of novel therapeutic and preventive strategies based on modulation of host cell signaling pathways, to maintain a status associated with gingival health, similar to progress that is being made in cancer therapy (Lemmon, 2003; Festuccia et al., 2005). In addition, certain host cell expression patterns could be exploited for use as diagnostic or prognostic indicators. This is particularly relevant in light of recent studies that suggest the involvement of several oral pathogens, including A. actinomycetemcomitans and P. gingivalis, in serious systemic conditions, cardiovascular diseases, and preterm delivery of low-birthweight infants (Offenbacher et al., 1998; Beck et al., 2005; Desvarieux et al., 2005; Demmer and Desvarieux, 2006).
Work performed herein was made possible by grants from the NIDCR [DE13523 and DE16715 (M.H.), DE11111 and DE14955 (R.J.L.)], and by T32 Training Grant DE07200. Additional funding was provided by the Center for Molecular Microbiology and the Department of Oral Biology, University of Florida College of Dentistry. The authors are grateful to Jeffrey J. Mans, Yoshiaki Hasegawa, and M. Cecilia Lopes, who performed many of the original array experiments (Handfield et al., 2005; Hasegawa et al., 2007). We thank Dr. Dolphine Oda (University of Washington) for kindly providing HIGK cells, and Dr. P. Fives-Taylor for A. actinomycetemcomitans strain VT1169. Analyses were performed with BRB ArrayTools developed by Dr. Richard Simon and Amy Peng Lam. Pathway Express (Khatri et al., 2005) was graciously made available by Intelligent Systems and Bioinformatics Laboratory, Computer Science Department, Wayne State University (Detroit, MI, USA), and is available at http://vortex.cs.wayne.edu/projects.htm.
A supplemental appendix to this article is published electronically only at http://jdr.iadrjournals.org/cgi/content/full/87/3/203/DC1. Received for publication August 1, 2007. Revision received November 20, 2007. Accepted for publication November 27, 2007.
Journal of Dental Research, Vol. 87, No. 3,
203-223 (2008) This article has been cited by other articles:
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ABSTRACT
INTRODUCTION
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