(494b) Discovery of Biomarkers Using High-Throughput Proteomics for Temporal Profiling of Periodontitis

       Periodontitis is a common, destructive disease of the periodontium that involves a disruption of the healthy homeostasis of the oral microbial population [1].  Disease onset is multi-faceted, and disease progression is influenced by environmental, systemic, and genetic risk factors [2].  Imbalanced host inflammatory responses to expanding bacterial populations can result in tooth loss and alveolar bone resorption [3].  In a clinical setting, bleeding on probing as a disease indicator is sensitive but not specific, while clinical attachment level can indicate disease presence but provides little information on disease progression and treatment efficacy [4,5].  The complexity of periodontitis has led to the search for clinically applicable molecular biomarkers for the diagnosis or staging of the disease.  In particular, temporal profiling of periodontitis for indicators of disease progression and treatment efficacy would have great value for clinical practitioners [4-7].

       We present the results of the first large-scale temporal proteomics study of periodontitis.  Pooled gingival crevicular fluid samples were collected from 10 patients diagnosed with chronic periodontitis over the course of a 13-week treatment program prepared for mass spectrometry analysis as previously described [8,9].  Tandem mass spectra were collected using an online high-performance liquid chromatography-nanoelectrospray-hybrid ion trap-Orbitrap platform.  Spectra were analyzed with the PILOT_PROTEIN proteomics software suite, which includes de novo peptide sequencing [10], sequence alignment and database search [11], and peptide-to-protein annotation algorithms [12].  From the thousands of candidate protein biomarkers, a small subset of promising biomarkers were extracted by temporal pattern matching and logistic function fitting.  We discuss these temporal profiling biomarkers as well as the automatic methods used to extract them from an expansive proteomics dataset.

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