Proteomic analysis of gingival crevicular fluid for discovery of novel periodontal disease markers

Application of quantitative proteomic analysis using tandem mass tags for discovery and identification of novel biomarkers in periodontal disease

Periodontal disease is a bacterial infection that destroys the gingiva and surrounding tissues of the oral cavity. Gingival crevicular fluid (GCF) is extracted from the gingival sulcus and pocket. Analysis of biochemical markers in GCF, which predict the progression of periodontal disease, may facilitate disease diagnosis. However, no useful GCF biochemical markers with high sensitivity for detecting periodontal disease have been identified. Thus, the search for biochemical markers of periodontal disease is of continued interest in experimental and clinical periodontal disease research. Using tandem mass tag (TMT) labeling, we analyzed GCF samples from healthy subjects and patients with periodontal disease, and identified a total of 619 GCF proteins based on proteomic analysis. Of these, we focused on two proteins, matrix metalloproteinase (MMP)-9 and neutrophil gelatinase-associated lipocalin (LCN2), which are involved in the progression of periodontal disease. Western blot analysis revealed that the levels of MMP-9 and LCN2 were significantly higher in patients with periodontal disease than in healthy subjects. In addition, ELISA also detected significantly higher levels of LCN2 in patients with periodontal disease than in healthy subjects. Thus, LC-MS/MS analyses of GCF using TMT labeling led to the identification of LCN2, which may be a promising GCF biomarker for the detection of periodontal disease.

Host genome, epigenome, and oral microbiome interactions: toward personalized periodontal therapy

Periodontal diseases are multidimensional and complex. Bacterial content is the initiator, but disease progression depends on genetic and environmental parameters related to the host. Although bone loss magnitude is the common resulting outcome, the biologic process likely represents a unique inflammatory response characteristic to every individual. Therefore, it is obvious that practitioners must take into account the influence of these parameters and tailor a treatment accordingly. New, emerging deoxyribonucleotide-based technologies allow integration of the biologic impact of the environment, and periodontists should be prepared to incorporate these technologies into their practice to advance personalized medicine. This commentary provides updated insights on the distinctiveness of inflammation per individual in terms of microbiome and genome specificity and cites some educational resources helpful for implementing individualized therapy.

Mass spectrometric analysis of gingival crevicular fluid biomarkers can predict periodontal disease progression

BACKGROUND AND OBJECTIVE

Gingival crevicular fluid has been suggested as a possible source of biomarkers for periodontal disease progression. This paper describes a technique for the analysis of gingival crevicular fluid from individual sites using mass spectrometry. It explores the novel use of mass spectrometry to examine the relationship between the relative amounts of proteins and peptides in gingival crevicular fluid and their relationship with clinical indices and periodontal attachment loss in periodontal maintenance patients. The aim of this paper was to assess whether the mass spectrometric analysis of gingival crevicular fluid may allow for the site-specific prediction of periodontal disease progression.

MATERIAL AND METHODS

Forty-one periodontal maintenance subjects were followed over 12 mo, with clinical measurements taken at baseline and every 3 mo thereafter. Gingival crevicular fluid was collected from subjects at each visit and was analysed using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. Samples were classified based upon pocket depth, modified gingival index (MGI), plaque index and attachment loss, and were analysed within these groups. A genetic algorithm was used to create a model based on pattern analysis to predict sites undergoing attachment loss.

RESULTS

Three hundred and eighty-five gingival crevicular fluid samples were analysed. Twenty-five sites under observation in 14 patients exhibited attachment loss of > 2 mm over the 12-mo period. The clinical indices pocket depth, MGI, plaque levels and bleeding on probing served as poor discriminators of gingival crevicular fluid mass spectra. Models generated from the gingival crevicular fluid mass spectra could predict attachment loss at a site with a high specificity (97% recognition capability and 67% cross-validation).

CONCLUSIONS

Gingival crevicular fluid mass spectra could be used to predict sites with attachment loss. The use of algorithm-generated models based on gingival crevicular fluid mass spectra may provide utility in the diagnosis of periodontal disease.