Cross-Sectional Association of Salivary Proteins with Age, Sex, Body Mass Index, Smoking, and Education.

Whole saliva is gaining more and more attention as a diagnostic tool to study disease-specific changes in human subjects. Prior to the actual disease-related analyses, it is important to understand the influence of various demographic variables and coupled phenotypes on salivary protein signatures. In a cross-sectional approach, we analyzed the influence of age, sex, body mass index (BMI), smoking, and education on salivary protein signatures in whole saliva samples of 187 individuals. Subjects were randomly selected from the population-based Study of Health in Pomerania (SHIP-Trend). Stimulated whole saliva was collected, and proteins were precipitated and proteolytically digested. Samples were analyzed by label-free tandem mass spectrometry. Of the 602 human proteins identified in at least 40% of the saliva samples, we used 304 proteins, which could be identified with at least two unique peptides, for statistical analyses. Univariate and multivariate linear models were used to reveal associations with the phenotypes. The largest number of proteins was associated with smoking status. Moreover, age had a distinct influence on the salivary protein composition. The study discloses the influence of common phenotypes on the salivary protein pattern of human subjects. These results should be considered when studying disease-related proteome signatures in saliva.

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

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.

Comparative analysis of Salivette® and paraffin gum preparations for establishment of a metaproteomics analysis pipeline for stimulated human saliva.

The value of saliva as a diagnostic tool can be increased by taxonomic and functional analyses of the microbiota as recently demonstrated. In this proof-of-principle study, we compare two collection methods (Salivette® (SV) and paraffin gum (PG)) for stimulated saliva from five healthy participants and present a workflow including PG preparation which is suitable for metaproteomics.