Beyond good and evil in the oral cavity: insights into host-microbe relationships derived from transcriptional profiling of gingival cells

Abstract

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.

Figure 1. Different patterns of gene expression of oral epithelial HIGK cells upon co-culture with oral species

Hierarchical clustering of variance-normalized gene expression data from uninfected human HIGK cells and from cells in co-culture with microorganisms for 2 h before RNA isolation and purification. Expression and variation filters were applied to the data set before clustering. Probe sets giving hybridization signal intensity at or below background levels on all arrays tested were eliminated from further analysis. The resulting data set was culled by ranking on the coefficient of variation and eliminating the bottom half of the data set to remove probe sets whose expression did not vary between the treatment regimens. The gene expression observations were variance normalized to a mean of 0 and a standard deviation of 1, and this normalized data set was subjected to hierarchical cluster analysis with average linkage clustering of the nodes. The variation in gene expression for a given gene is expressed as distance from the mean observation for that gene. Each expression data point represents the ratio of the fluorescence intensity of the cRNA from A. actinomycetemcomitans infected (columns Aa), P. gingivalis-infected (columns Pg) F. nucleatum-infected (column Fn) and S. gordonii-infected (column Sg) to the fluorescence intensity of the cRNA from mock-infected HIGK cells (columns CTRL). The scale adjacent to the dendrogram relates to Pearson’s correlation coefficient. Re-normalized from array data previously reported in Handfield et al. (2005) and Hasegawa et al. (2007), following procedure described in supplementary information.

Figure 2. Ontology analysis of selected pathways of infected HIGK Cells

2.1 Regulation of the Actin Cytoskeleton; 2.2 MAPK Signaling Pathway. BRB Array Tools was used to generate probesets differentially regulated at the P<0.05 level of significance. The geometric mean signal intensity level for probesets passing this threshold were analyzed with Pathway Express software to populate known KEGG pathways according to transcriptional profiles obtained from GeneChip experiments. HIGK response of infected vs controls for A) S. gordonii, B) F. nucleatum, C) A. actinomycetemcomitans and D) P. gingivalis. Genes shown in red are transcriptionally induced compared to the baseline level, while genes in blue are transcriptionally down-regulated. Genes in green are unchanged at the P<0.05 significance level.

Figure 2. Ontology analysis of selected pathways of infected HIGK Cells

2.1 Regulation of the Actin Cytoskeleton; 2.2 MAPK Signaling Pathway. BRB Array Tools was used to generate probesets differentially regulated at the P<0.05 level of significance. The geometric mean signal intensity level for probesets passing this threshold were analyzed with Pathway Express software to populate known KEGG pathways according to transcriptional profiles obtained from GeneChip experiments. HIGK response of infected vs controls for A) S. gordonii, B) F. nucleatum, C) A. actinomycetemcomitans and D) P. gingivalis. Genes shown in red are transcriptionally induced compared to the baseline level, while genes in blue are transcriptionally down-regulated. Genes in green are unchanged at the P<0.05 significance level.

Figure 2. Ontology analysis of selected pathways of infected HIGK Cells

2.1 Regulation of the Actin Cytoskeleton; 2.2 MAPK Signaling Pathway. BRB Array Tools was used to generate probesets differentially regulated at the P<0.05 level of significance. The geometric mean signal intensity level for probesets passing this threshold were analyzed with Pathway Express software to populate known KEGG pathways according to transcriptional profiles obtained from GeneChip experiments. HIGK response of infected vs controls for A) S. gordonii, B) F. nucleatum, C) A. actinomycetemcomitans and D) P. gingivalis. Genes shown in red are transcriptionally induced compared to the baseline level, while genes in blue are transcriptionally down-regulated. Genes in green are unchanged at the P<0.05 significance level.

Figure 2. Ontology analysis of selected pathways of infected HIGK Cells

2.1 Regulation of the Actin Cytoskeleton; 2.2 MAPK Signaling Pathway. BRB Array Tools was used to generate probesets differentially regulated at the P<0.05 level of significance. The geometric mean signal intensity level for probesets passing this threshold were analyzed with Pathway Express software to populate known KEGG pathways according to transcriptional profiles obtained from GeneChip experiments. HIGK response of infected vs controls for A) S. gordonii, B) F. nucleatum, C) A. actinomycetemcomitans and D) P. gingivalis. Genes shown in red are transcriptionally induced compared to the baseline level, while genes in blue are transcriptionally down-regulated. Genes in green are unchanged at the P<0.05 significance level.