Secretome of gingival epithelium in response to subgingival biofilms

Periodontitis is the chronic inflammatory destruction of periodontal tissues as a result of bacterial biofilm formation on the tooth surface. Proteins secreted by the gingival epithelium challenged by subgingival biofilms represent an important initial response for periodontal inflammation. The aim of this in vitro study was to characterize the whole secreted proteome of gingival epithelial tissue challenged by subgingival biofilms, and to evaluate the differential effects of the presence of the red-complex species in the biofilm. Multi-layered human gingival epithelial cultures were challenged with a 10-species in vitro biofilm model or its seven-species variant excluding the red complex. Liquid chromatography-tandem mass spectrometry for label-free quantitative proteomics was applied to identify and quantify the secreted epithelial proteins in the culture supernatant. A total of 192 proteins were identified and quantified. The biofilm challenge resulted in more secreted proteins being downregulated than upregulated. Even so, presence of the red complex in the biofilm was responsible for much of this downregulatory effect. Over 24 h, the upregulated biological processes were associated with inflammation and apoptosis, whereas the downregulated processes were associated with the disruption of epithelial tissue integrity and impairment of tissue turnover. Over 48 h, negative regulation of several metabolic processes and degradation of various molecular complexes was further intensified. Again, many of these biological regulations were attributed to the presence of the red complex. In conclusion, the present study provides the secreted proteome profile of gingival epithelial tissue to subgingival biofilms, and identifies a significant role for the red-complex species in the observed effects.

Detection and correction of interference in SRM analysis

Selected Reaction Monitoring (SRM) is a method of choice for accurate quantitation of low-abundance proteins in complex backgrounds. This strategy is, however, sensitive to interference from other components in the sample that have the same precursor and fragment masses as the monitored transitions. We present here an approach to detect interference by using the expected relative intensity of SRM transitions. We also designed an algorithm to automatically detect the linear range of calibration curves. These approaches were applied to the experimental data of Clinical Proteomic Tumor Analysis Consortium (CPTAC) Verification Work Group Study 7 and show that the corrected measurements provide more accurate quantitation than the uncorrected data.