382426 Discovery of Biomarkers Using High-Throughput Proteomics for Temporal Profiling of Periodontitis

Wednesday, November 19, 2014: 12:52 PM
206 (Hilton Atlanta)
Yannis A. Guzman1, Dimitra Sakellari2 and Christodoulos A. Floudas1, (1)Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, (2)Department of Preventive Denistry, Periodontology and Implant Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece

       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.


1. Kumar PS, Leys EJ, Bryk JM, Martinez FJ, Moeschberger ML, Griffen AL. Changes in periodontal health status are associated with bacterial community shifts as assessed by quantitative 16S cloning and sequencing. J Clin Microbiol. 44(10), 3665–73 (2006).

2. Kornman KS. Mapping the pathogenesis of periodontitis: a new look. J Periodontol. 79(8S), 1560–8 (2008).

3. Page RC, Kornman KS. The host response to the microbial challenge in periodontitis: assembling the players. Periodontol 2000. 14(1), 9–11 (1997).

4. Zhang L, Henson BS, Camargo PM, Wong DT. The clinical value of salivary biomarkers for periodontal disease. Periodontol 2000. 51(1), 25–37 (2009).

5. Loos BG, Tjoa S. Host-derived diagnostic markers for periodontitis: do they exist in gingival crevice fluid? Periodontol 2000. 39(1), 53–72 (2005).

6. Buduneli N, Kinane DF. Host-derived diagnostic markers related to soft tissue destruction and bone degradation in periodontitis. J Clin Periodontol. 38(S11), 85–105 (2011).

7. Guzman YA, Sakellari D, Floudas CA. Proteomics for the discovery of biomarkers and diagnosis of periodontitis: a critical review. Expert Rev Proteomics. 11(1), 31–41 (2014).

8. Baliban RC, Sakellari D, Li Z, DiMaggio PA, Garcia BA, Floudas CA. Novel protein identification methods for biomarker discovery via a proteomic analysis of periodontally healthy and diseased gingival crevicular fluid samples. J Clin Periodontol. 39(3), 203–12 (2012).

9. Baliban RC, Sakellari D, Li Z, Guzman YA, Garcia BA, Floudas CA. Discovery of biomarker combinations that predict periodontal health or disease with high accuracy from GCF samples based on high-throughput proteomic analysis and mixed-integer linear optimization. J Clin Periodontol. 40(2), 131–9 (2013).

10. DiMaggio PA, Floudas CA. De novo peptide identification via tandem mass spectrometry and integer linear optimization. Anal Chem. 79(4), 1433–46 (2007).

11. DiMaggio PA, Floudas CA, Lu B, Yates JR 3rd. A hybrid method for peptide identification using integer linear optimization, local database search, and quadrupole time-of-flight or OrbiTrap tandem mass spectrometry. J Proteome Res. 7(4), 1584–93 (2008).

12. Baliban RC, DiMaggio PA, Plazas-Mayorca MD, Garcia BA, Floudas CA. PILOT_PROTEIN: identification of unmodified and modified proteins via high-resolution mass spectrometry and mixed-integer linear optimization. J Proteome Res. 11(9), 4615–29 (2012).

Extended Abstract: File Not Uploaded