Periodontal disease is a bacterial infection of the gums and bone associated with the teeth. Early stage infection, called gingivitis, manifests as swollen and bleeding gums. Periodontitis is the advanced stage oral disease, when the gums pull away from the teeth. Bone and tooth loss are highly likely.
Gingival crevicular fluid (GCF) present in the gingival crevices and periodontal pockets contains proteins from the host and the bacterial infection. Analysis of these proteins may yield useful biomarkers to predict the progression and diagnosis of periodontal disease. Studies suggest that the proteomic approach is helpful in the discovery and identification of the disease’s biochemical markers in GCF.1,2 Researchers led by Dr. Fumio Nomura, Department of Molecular Diagnosis (F8) at the Graduate School of Medicine in Chiba University (Japan), have therefore analyzed GCF samples from healthy subjects and patients with periodontal disease, using tandem mass tag (TMT) chemical labeling. The researchers state that TMT can identify useful GCF biochemical markers to detect periodontal disease with high sensitivity.3
TMTs are used for mass spectrometry (MS)-based comparative proteomics; they enable the identification and quantification of biological molecules in cells tissues, and body fluids, using gel-free proteomic approaches. The technology enables concurrent multiplexing of several samples (up to six) in a single experiment.4,5,6
The researchers obtained GCF from 31 patients (17 males and 14 females, mean age of 46.3 years) with chronic adult periodontal disease. They randomly divided patients into two groups: a test set for proteomic analysis using TMT, and a validation set. The test set comprised GCF from four patients and GCF from two healthy volunteers; the validation set consisted of GCF from 27 patients and GCF from 14 healthy volunteers. The investigators carried out protein extraction, reduction and alkylation, and enzymatic in-solution digestion of proteins, followed by TMT labeling according to established protocols and procedures.
The researchers labeled GCF samples from two healthy subjects using TMT with reporter ions at m/z = 126,127, GCF samples from two patients with mild to moderate periodontal disease using TMT with reporter ions at m/z = 128,129, and GCF samples from two other patients with severe periodontal disease using TMT with reporter ions at m/z = 130,131. They fractionated the TMT-labeled peptides using high-pH reversed-phase high-performance liquid chromatography (HPLC), lyophilized and then subjected to LC-MS/MS analysis using an LTQ Orbitrap XL mass spectrometer (Thermo Scientific, Waltham, MA).
Using a database search engine (Proteome Discoverer, Thermo Scientific, San Jose, CA) the researchers identified and quantified proteins from the mass, tandem mass and reporter ion spectra of peptides. Using specific search parameters, the investigators matched peptide mass data by searching the Human International Protein Index database (IPI, July 2008, 72,079 entries, European Bioinformatics Institute).
The findings from proteomic analysis revealed 619 proteins with unique peptide sequences. In patients with severe periodontal disease, the levels of 39 proteins were increased by five-fold, unlike the healthy controls. The team used western blot analysis and enzyme-linked immunosorbent assay (ELISA) to confirm the increased expression of proteins linked to severe periodontal disease progression. Although the levels of small proline-rich protein 3 (SPRR3) and cystatin B were only slightly elevated, the levels of matrix metalloproteinase-9 (MMP-9) and neutrophil gelatinase-associated lipocalin (LCN2) were significantly higher in patients with periodontal disease than in healthy subjects. MMP-9 is a zinc-dependent endopeptidase secreted by alveolar macrophages and granulocytes. It degrades type I, II and III collagen.7 MMP-9 causes the connective tissue degradation in periodontal disease. Bacterial infection in periodontal disease stimulates macrophages and lymphocytes to produce inflammatory mediators that drive fibroblasts to produce MMPs. Indeed, the MMP-9 levels in chronic periodontitis GCF samples in the current study were twice the levels seen in healthy controls.
LCN2 is a component of neutrophil granules occurring in tissues exposed to microorganisms. LCN2 is an antibacterial peptide of natural immunity. Inflammation boosts the expression of LCN2. LCN2 acts by capturing iron particles and transporting them to the cytoplasm after binding with specific membrane receptors such as 24p3R and megalin. The researchers speculate that LCN2 inhibits the growth of bacteria causing periodontal disease by interfering with siderophore-mediated iron acquisition. The investigators argue that LCN2 interacts with MMP-9 and that the LCN2/MMP-9 complex mediates the periodontal tissue degradation. A previous report suggests that LCN2 protects MMP-9 from proteolytic degradation.8
The results also revealed increased levels of membrane proteins such as leptin receptor and TLR6 in the GCF of patients with severe periodontal disease. The levels of annexin 3 and TLR7 increased more than five-fold in patients with severe periodontal disease, as compared with healthy controls. Annexin 3 is a phospholipid-binding protein inhibiting the interactions of bacterial endotoxin with cellular receptors. Membrane proteins play a key role as biomarkers for early diagnosis, disease progression, and targets for drug delivery. The researchers therefore hope to develop an appropriate strategy for membrane proteome analysis in periodontal research in the future.
The researchers argue that the TMT approach enables the discovery of less abundant proteins that might otherwise be missed using conventional proteomic techniques. “As quantitative proteomic technologies have been advancing rapidly, an increasing number of novel periodontal disease markers are expected to be identified in the near future,” they conclude.
- Bostanci, N., et al. (2010) “Application of label-free absolute quantitative proteomics in human gingival crevicular fluid by LC/MSE (gingival exudatome),” Journal of Proteome Research, 9 (pp. 2191–9), doi: 10.1021/pr900941z.
- Baliban, R.C., et al. (2012) “Novel protein identification methods for biomarker discovery via a proteomic analysis of periodontally healthy and diseased gingival crevicular fluid samples,” Journal of Clinical Periodontology, 39(3) (pp. 203–12), doi: 10.1111/j.1600-051X.2011.01805.x.
- Tsuchida, S., et al. (2013, May 21) “Application of quantitative proteomic analysis using tandem mass tags for discovery and identification of novel biomarkers in periodontal disease,” Proteomics, doi: 10.1002/pmic.201200510.
- Arntzen, M.Ø., et al. (2011) “Software for isobaric quantitative proteomics using IPTL, iTRAQ, and TMT,” Journal of Proteome Research, 10(2) (pp. 913–20), doi: 10.1021/pr1009977. E-pub 2010 Nov 29.
- Giron, P., et al. (2011) “Quantitative analysis of human cerebrospinal fluid proteins using a combination of cysteine tagging and amine-reactive isobaric labeling,” Journal of Proteome Research, 10(1) (pp. 249–58), doi: 10.1021/pr100535f.
- Baeumlisberger, D., et al. (2010) “Labeling elastase digests with TMT: Informational gain by identification of poorly detectable peptides with MALDI-TOF/TOF mass spectrometry,” Proteomics, 10(21) (pp. 3905–9), doi: 10.1002/pmic.201000288.
- Giannobile, W.V. (2008) “Host-response therapeutics for periodontal diseases,” Journal of Periodontology, 79 (pp. 1592–600), doi: 10.1902/jop.2008.080174.
- Bolignano, D., et al. (2010) “Neutrophil gelatinase-associated lipocalin (NGAL) in human neoplasias: A new protein enters the scene,” Cancer Letters, 288 (pp. 10–16), doi:10.1016/j.canlet.2009.05.027.
Post Author: Sridhar Nadamuni.