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Insulin Response Genes in Different Stages of Periodontal Disease.

ABSTRACT: Bacterial infections are known to alter glucose metabolism within tissues via mechanisms of inflammation. We conducted this study to examine whether insulin response genes are differentially expressed in gingival tissues, comparing samples from experimental gingivitis and periodontitis subjects to those from healthy individuals. Total RNA was extracted from gingival biopsies from 26 participants: 8 periodontally healthy, 9 experimental gingivitis, and 9 periodontitis subjects. Gene expression patterns were evaluated with a polymerase chain reaction array panel to examine 84 candidate genes involved with glucose metabolism, insulin resistance, and obesity. Array data were evaluated with a t test adjusted by the false discover rate (P < 0.05), and ingenuity pathway analysis was performed for statistical testing of pathways. Although tissue samples were not sufficient to enable protein quantification, we confirmed the upregulation of the key gene using lipopolysaccharide-stimulated primary gingival epithelial cells by Western blot. The mRNA expression patterns of genes that are associated with insulin response and glucose metabolism are markedly different in experimental gingivitis subjects compared with healthy controls. Thirty-two genes are upregulated significantly by at least 2-fold, adjusted for false discover rate (P < 0.05). Periodontitis subjects show similar but attenuated changes in gene expression patterns, and no genes meet the significance criteria. Ingenuity pathway analysis demonstrates significant activation of the carbohydrate metabolism network in experimental gingivitis but not in periodontitis. G6PD protein increases in response to lipopolysaccharide stimulation in primary gingival epithelial cells, which is in the same direction as upregulated mRNA in tissues. Acute gingival inflammation may be associated with tissue metabolism changes, but these changes are not evident in chronic periodontitis. This study suggests that acute gingival inflammation may induce localized changes that modify tissue insulin/glucose metabolism.

SUBMITTER: Yu N

PROVIDER: S-EPMC4547205 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Insulin Response Genes in Different Stages of Periodontal Disease.

Yu N N Barros S P SP Zhang S S Moss K L KL Phillips S T ST Offenbacher S S

Journal of dental research 20150429 9 Suppl

Bacterial infections are known to alter glucose metabolism within tissues via mechanisms of inflammation. We conducted this study to examine whether insulin response genes are differentially expressed in gingival tissues, comparing samples from experimental gingivitis and periodontitis subjects to those from healthy individuals. Total RNA was extracted from gingival biopsies from 26 participants: 8 periodontally healthy, 9 experimental gingivitis, and 9 periodontitis subjects. Gene expression pa ...[more]

PMID: 25924856

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Project description:Periodontitis is the major cause of tooth loss in adults and is linked to systemic illnesses, such as cardiovascular disease and stroke. The development of rapid point-of-care (POC) chairside diagnostics has the potential for the early detection of periodontal infection and progression to identify incipient disease and reduce health care costs. However, validation of effective diagnostics requires the identification and verification of biomarkers correlated with disease progression. This clinical study sought to determine the ability of putative host- and microbially derived biomarkers to identify periodontal disease status from whole saliva and plaque biofilm.One hundred human subjects were equally recruited into a healthy/gingivitis group or a periodontitis population. Whole saliva was collected from all subjects and analyzed using antibody arrays to measure the levels of multiple proinflammatory cytokines and bone resorptive/turnover markers.Salivary biomarker data were correlated to comprehensive clinical, radiographic, and microbial plaque biofilm levels measured by quantitative polymerase chain reaction (qPCR) for the generation of models for periodontal disease identification. Significantly elevated levels of matrix metalloproteinase (MMP)-8 and -9 were found in subjects with advanced periodontitis with Random Forest importance scores of 7.1 and 5.1, respectively. The generation of receiver operating characteristic curves demonstrated that permutations of salivary biomarkers and pathogen biofilm values augmented the prediction of disease category. Multiple combinations of salivary biomarkers (especially MMP-8 and -9 and osteoprotegerin) combined with red-complex anaerobic periodontal pathogens (such as Porphyromonas gingivalis or Treponema denticola) provided highly accurate predictions of periodontal disease category. Elevated salivary MMP-8 and T. denticola biofilm levels displayed robust combinatorial characteristics in predicting periodontal disease severity (area under the curve = 0.88; odds ratio = 24.6; 95% confidence interval: 5.2 to 116.5).Using qPCR and sensitive immunoassays, we identified host- and bacterially derived biomarkers correlated with periodontal disease. This approach offers significant potential for the discovery of biomarker signatures useful in the development of rapid POC chairside diagnostics for oral and systemic diseases. Studies are ongoing to apply this approach to the longitudinal predictions of disease activity.

Dataset Information

Insulin Response Genes in Different Stages of Periodontal Disease.

Publications

Insulin Response Genes in Different Stages of Periodontal Disease.

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