Cell Type-Specific Decomposition of Gingival Tissue Transcriptomes.

Genome-wide transcriptomic analyses in whole tissues reflect the aggregate gene expression in heterogeneous cell populations comprising resident and migratory cells, and they are unable to identify cell type-specific information. We used a computational method (population-specific expression analysis [PSEA]) to decompose gene expression in gingival tissues into cell type-specific signatures for 8 cell types (epithelial cells, fibroblasts, endothelial cells, neutrophils, monocytes/macrophages, plasma cells, T cells, and B cells). We used a gene expression data set generated using microarrays from 120 persons (310 tissue samples; 241 periodontitis affected and 69 healthy). Decomposition of the whole-tissue transcriptomes identified differentially expressed genes in each of the cell types, which mapped to biologically relevant pathways, including dysregulation of Th17 cell differentiation, AGE-RAGE signaling, and epithelial-mesenchymal transition in epithelial cells. We validated selected PSEA-predicted, differentially expressed genes in purified gingival epithelial cells and B cells from an unrelated cohort (n = 15 persons), each of whom contributed with 1 periodontitis-affected and 1 healthy gingival tissue sample. Differential expression of these genes by quantitative reverse transcription polymerase chain reaction corroborated the PSEA predictions and pointed to dysregulation of biologically important pathways in periodontitis. Collectively, our results demonstrate the robustness of the PSEA in the decomposition of gingival tissue transcriptomes and its ability to identify differentially regulated transcripts in particular cellular constituents. These genes may serve as candidates for further investigation with respect to their roles in the pathogenesis of periodontitis.

Identification of Master Regulator Genes in Human Periodontitis.

Analytic approaches confined to fold-change comparisons of gene expression patterns between states of health and disease are unable to distinguish between primary causal disease drivers and secondary noncausal events. Genome-wide reverse engineering approaches can facilitate the identification of candidate genes that may distinguish between causal and associative interactions and may account for the emergence or maintenance of pathologic phenotypes. In this work, we used the algorithm for the reconstruction of accurate cellular networks (ARACNE) to analyze a large gene expression profile data set (313 gingival tissue samples from a cross-sectional study of 120 periodontitis patients) obtained from clinically healthy (n = 70) or periodontitis-affected (n = 243) gingival sites. The generated transcriptional regulatory network of the gingival interactome was subsequently interrogated with the master regulator inference algorithm (MARINA) and gene expression signature data from healthy and periodontitis-affected gingiva. Our analyses identified 41 consensus master regulator genes (MRs), the regulons of which comprised between 25 and 833 genes. Regulons of 7 MRs (HCLS1, ZNF823, XBP1, ZNF750, RORA, TFAP2C, and ZNF57) included >500 genes each. Gene set enrichment analysis indicated differential expression of these regulons in gingival health versus disease with a type 1 error between 2% and 0.5% and with >80% of the regulon genes in the leading edge. Ingenuity pathway analysis showed significant enrichment of 36 regulons for several pathways, while 6 regulons (those of MRs HCLS1, IKZF3, ETS1, NHLH2, POU2F2, and VAV1) were enriched for >10 pathways. Pathways related to immune system signaling and development were the ones most frequently enriched across all regulons. The unbiased analysis of genome-wide regulatory networks can enhance our understanding of the pathobiology of human periodontitis and, after appropriate validation, ultimately identify target molecules of diagnostic, prognostic, or therapeutic value.

Gingival tissue transcriptomes identify distinct periodontitis phenotypes.

The currently recognized principal forms of periodontitis-chronic and aggressive-lack an unequivocal, pathobiology-based foundation. We explored whether gingival tissue transcriptomes can serve as the basis for an alternative classification of periodontitis. We used cross-sectional whole-genome gene expression data from 241 gingival tissue biopsies obtained from sites with periodontal pathology in 120 systemically healthy nonsmokers with periodontitis, with available data on clinical periodontal status, subgingival microbial profiles, and serum IgG antibodies to periodontal microbiota. Adjusted model-based clustering of transcriptomic data using finite mixtures generated two distinct clusters of patients that did not align with the current classification of chronic and aggressive periodontitis. Differential expression profiles primarily related to cell proliferation in cluster 1 and to lymphocyte activation and unfolded protein responses in cluster 2. Patients in the two clusters did not differ with respect to age but presented with distinct phenotypes (statistically significantly different whole-mouth clinical measures of extent/severity, subgingival microbial burden by several species, and selected serum antibody responses). Patients in cluster 2 showed more extensive/severe disease and were more often male. The findings suggest that distinct gene expression signatures in pathologic gingival tissues translate into phenotypic differences and can provide a basis for a novel classification.

Molecular differences between chronic and aggressive periodontitis.

The 2 major forms of periodontitis, chronic (CP) and aggressive (AgP), do not display sufficiently distinct histopathological characteristics or microbiological/immunological features. We used molecular profiling to explore biological differences between CP and AgP and subsequently carried out supervised classification using machine-learning algorithms including an internal validation. We used whole-genome gene expression profiles from 310 'healthy' or 'diseased' gingival tissue biopsies from 120 systemically healthy non-smokers, 65 with CP and 55 with AgP, each contributing with ≥ 2 'diseased' gingival papillae (n = 241; with bleeding-on-probing, probing depth ≥ 4 mm, and clinical attachment loss ≥ 3 mm), and, when available, a 'healthy' papilla (n = 69; no bleeding-on-probing, probing depth ≤ 4 mm, and clinical attachment loss ≤ 4 mm). Our analyses revealed limited differences between the gingival tissue transcriptional profiles of AgP and CP, with genes related to immune responses, apoptosis, and signal transduction overexpressed in AgP, and genes related to epithelial integrity and metabolism overexpressed in CP. Different classifying algorithms discriminated CP from AgP with an area under the curve ranging from 0.63 to 0.99. The small differences in gene expression and the highly variable classifier performance suggest limited dissimilarities between established AgP and CP lesions. Future analyses may facilitate the development of a novel, 'intrinsic' classification of periodontitis based on molecular profiling.

MicroRNAs and their target genes in gingival tissues.

To gain insights into the in vivo function of miRNAs in the context of periodontitis, we examined the occurrence of miRNAs in healthy and diseased gingival tissues and validated their in silico-predicted targets through mRNA profiling using whole-genome microarrays in the same specimens. Eighty-six individuals with periodontitis contributed 198 gingival papillae: 158 'diseased' (bleeding-on-probing, PD > 4 mm, and AL ≥ 3 mm) and 40 'healthy' (no bleeding, PD ≤ 4 mm, and AL ≤ 2 mm). Expression of 1,205 miRNAs was assessed by microarrays, followed by selected confirmation by quantitative RT-PCR. Predicted miRNA targets were identified and tested for enrichment by Gene Set Enrichment Analysis (GSEA). Enriched gene sets were grouped in functional categories by DAVID and Ingenuity Pathway Analysis. One hundred fifty-nine miRNAs were significantly differentially expressed between healthy and diseased gingiva. Four miRNAs (hsa-miR-451, hsa-miR-223, hsa-miR-486-5p, hsa-miR-3917) were significantly overexpressed, and 7 (hsa-miR-1246, hsa-miR-1260, hsa-miR-141, hsa-miR-1260b, hsa-miR-203, hsa-miR-210, hsa-miR-205*) were underexpressed by > 2-fold in diseased vs. healthy gingiva. GSEA and additional filtering identified 60 enriched miRNA gene sets with target genes involved in immune/inflammatory responses and tissue homeostasis. This is the first study that concurrently examined miRNA and mRNA expression in gingival tissues and will inform mechanistic experimentation to dissect the role of miRNAs in periodontal tissue homeostasis and pathology.

[Relationship between periodontitis and systemic diseases]. / Wechselwirkungen zwischen Parodontitis und systemischen Erkrankungen.

Periodontitis is a biofilm-induced inflammatory disease affecting the periodontium with a high and even increasing prevalence in the German population. During recent years, there is emerging evidence for systemic effects of a periodontal infection, in particular in relation to diabetes and atherosclerosis. There is a bi-directional relationship between periodontitis and diabetes. Diabetes promotes the occurrence, the progression, and the severity of periodontitis. The periodontal infection complicates the glycemic control in diabetes, increases the risk of diabetes-associated complications and possibly even of its onset. As a consequence, the treatment of periodontal infections should become an integral part of the management of diabetes, whereas glycemic control is a prerequisite for successful periodontal therapy. Periodontal infections are considered as independent risk factor for atherosclerosis and their clinical sequelae, e.g., cerebro- and cardiovascular diseases. The positive association is only moderate, however remarkably consistent. Periodontal therapy can result in positive effects on subclinical markers of atherosclerosis.

"Gum bug, leave my heart alone!"--epidemiologic and mechanistic evidence linking periodontal infections and atherosclerosis.

Evidence from epidemiologic studies suggests that periodontal infections are independently associated with subclinical and clinical atherosclerotic vascular disease. Although the strength of the reported associations is modest, the consistency of the data across diverse populations and a variety of exposure and outcome variables suggests that the findings are not spurious or attributable only to the effects of confounders. Analysis of limited data from interventional studies suggests that periodontal treatment generally results in favorable effects on subclinical markers of atherosclerosis, although such analysis also indicates considerable heterogeneity in responses. Experimental mechanistic in vitro and in vivo studies have established the plausibility of a link between periodontal infections and atherogenesis, and have identified biological pathways by which these effects may be mediated. However, the utilized models are mostly mono-infections of host cells by a limited number of 'model' periodontal pathogens, and therefore may not adequately portray human periodontitis as a polymicrobial, biofilm-mediated disease. Future research must identify in vivo pathways in humans that may (i) lead to periodontitis-induced atherogenesis, or (ii) result in treatment-induced reduction of atherosclerosis risk. Data from these studies will be essential for determining whether periodontal interventions have a role in the primary or secondary prevention of atherosclerosis.

Enhanced monocyte migration and pro-inflammatory cytokine production by Porphyromonas gingivalis infection.

BACKGROUND AND OBJECTIVE: Porphyromonas gingivalis, a major periodontal pathogen, has been reported to be involved in atherogenesis. In order to further understand this pathogen's link with systemic inflammation and vascular disease, we investigated its influence on murine monocytes and macrophages from three different sources. MATERIAL AND METHODS: Concanavalin A-elicited peritoneal macrophages, peripheral blood monocyte-derived macrophages and WEHI 274.1 monocytes were infected with either P. gingivalis 381 or its non-invasive fimbriae-deficient mutant, DPG3. RESULTS: Infection with P. gingivalis 381 markedly induced monocyte migration and significantly enhanced production of the pro-inflammatory cytokines, tumor necrosis factor-alpha and interleukin-6. Consistent with a role for this pathogen's major fimbriae and/or its invasive capacity, infection with DPG3 had a minimal effect on both monocyte attraction and pro-inflammatory cytokine production. CONCLUSION: Since monocyte recruitment and activation are important steps in the development of vascular inflammation and atherosclerosis, these results suggest that P. gingivalis infection may be involved in these processes.

Granulocyte chemotactic protein 2 (gcp-2/cxcl6) complements interleukin-8 in periodontal disease.

BACKGROUND AND OBJECTIVE: Mucosal inflammatory responses are orchestrated largely by pro-inflammatory chemokines. The chemokine granulocyte chemotactic protein 2 (CXCL6) is involved in neutrophil recruitment and migration. Previous studies have shown that granulocyte chemotactic protein 2 is up-regulated during mucosal inflammation (e.g. in inflammatory bowel disease), similarly to the functionally and structurally related chemokine interleukin-8. Nevertheless, unlike interleukin-8, a role of granulocyte chemotactic protein 2 in gingival inflammation has not been yet demonstrated. In this study we aimed to evaluate the expression of the chemokine granulocyte chemotactic protein 2 in clinically healthy vs. diseased gingival tissues and to explore possible correlations with clinical and microbiological markers of periodontitis. MATERIAL AND METHODS: Gene expression in 184 'diseased' and 63 'healthy' gingival tissue specimens from 90 patients with periodontitis was analyzed using Affymetrix U133Plus2.0 arrays. The expression of granulocyte chemotactic protein 2 was further confirmed by real-time reverse transcription-polymerase chain reaction, western blotting and enzyme-linked immunosorbent assay, while the localization of granulocyte chemotactic protein 2 in gingival tissues was analyzed by immunohistochemistry. Plaque samples from the adjacent periodontal pockets were collected and evaluated for 11 species of periodontal bacteria using checkerboard DNA-DNA hybridizations. RESULTS: Among all known chemokines, GCP-2 expression was the most up-regulated (3.8-fold, p < 1.1 x 10(-16)), in 'diseased' vs. 'healthy' tissue as compared to a 2.6-fold increased expression of interleukin-8 mRNA (p < 1.2 x 10(-15)). Increased expression of granulocyte chemotactic protein 2 correlated with higher levels of 'red' and 'orange' complex pathogens and with increased probing depth, but not with attachment loss. Immunohistochemistry showed that granulocyte chemotactic protein 2 was expressed in gingival vascular endothelium. CONCLUSION: The level of expression of granulocyte chemotactic protein 2 correlates with the severity of periodontitis and appears to act as a hitherto unrecognized functional adjunct to interleukin-8 in diseased gingival tissues.