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J Clin Periodontol. 2013 Sep;40(9):825-32. doi: 10.1111/jcpe.12130. Epub 2013 Jun 23.

Identification of periodontitis associated changes in the proteome of whole human saliva by mass spectrometric analysis.

Author information

1
Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany. manuela.gesell@uni-greifswald.de

Abstract

AIM:

Interest in human saliva proteomics for disease-specific biomarker screening increased in the last decade. We used whole saliva samples from periodontally healthy and diseased subjects with chronic periodontitis to screen for disease-associated differences in the protein pattern.

MATERIAL AND METHODS:

We selected 20 periodontally healthy and 20 periodontally diseased subjects from the population-based cross-sectional Study of Health in Pomerania (SHIP-2 and SHIP-Trend). Saliva collection was performed with commercially available Salivette(®) (Sarstedt, Nümbrecht, Germany). Whole saliva proteins were analysed after trichloroacetic acid (TCA) precipitation and proteolytic digestion with trypsin by LC-MS/MS. MS-data were analysed and quantified using the Rosetta Elucidator software package.

RESULTS:

In whole saliva we identified 344 human protein groups across all samples. For label free quantitation we only considered 152 proteins identified with more than one unique peptide. In total, 20 proteins showed 1.5-fold difference in abundance between controls and patients (p < 0.05); the majority of these proteins showed higher abundance in the periodontally diseased subjects. Functional annotation of proteins linked the periodontally diseased status with acute phase response and inflammatory processes.

CONCLUSION:

Label free proteomic analysis of whole saliva is a powerful tool to characterize the periodontal disease status and differentiate between healthy and periodontally diseased subjects.

KEYWORDS:

LC-MS/MS; label free quantitation; periodontitis; protein candidates; whole saliva proteomics

PMID:
23790309
DOI:
10.1111/jcpe.12130
[Indexed for MEDLINE]

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