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High‐throughput proteomic analysis of candidate biomarker changes in gingival crevicular fluid after treatment of chronic periodontitis
Author(s) -
Guzman Y. A.,
Sakellari D.,
Papadimitriou K.,
Floudas C. A.
Publication year - 2018
Publication title -
journal of periodontal research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.31
H-Index - 83
eISSN - 1600-0765
pISSN - 0022-3484
DOI - 10.1111/jre.12575
Subject(s) - periodontitis , medicine , biomarker , chronic periodontitis , bleeding on probing , proteomics , biomarker discovery , wilcoxon signed rank test , bioinformatics , biology , mann–whitney u test , biochemistry , gene
Background and Objective Untargeted, high‐throughput proteomics methodologies have great potential to aid in identifying biomarkers for the diagnosis of periodontal disease. The application of such methods to the discovery of candidate biomarkers for the resolution of periodontal inflammation after periodontal therapy has been investigated. Material and Methods Gingival crevicular fluid samples were collected from 10 patients diagnosed with chronic periodontitis at baseline and 1, 5, 9 and 13 weeks after completion of mechanical periodontal treatment. Clinical indices of periodontal disease, including probing depth, recession, clinical attachment level and bleeding on probing, were recorded at baseline and 13 weeks. Samples were analyzed using an online liquid chromatography‐nanoelectrospray‐hybrid ion trap‐Orbitrap mass spectrometer. Spectra were processed with the PILOT _ PROTEIN proteomics software suite. Results Clinical parameters were significantly improved 13 weeks after treatment (Wilcoxon signed ranks test, P < .05). From the substantial number of identified proteins, a small subset was extracted by filter methods that included temporal pattern matching, logistic function fitting and mixed‐integer linear optimization. This subset includes azurocidin, lysozyme C and myosin‐9 as candidate biomarkers prominent at baseline and alpha‐smooth muscle actin as prominent 13 weeks after treatment. Cross‐validation studies yielded average predictive accuracy and area under the curve of 0.900 and 0.930, respectively. Conclusion High‐throughput proteomic analysis can contribute to identifying endpoints of periodontal therapy. These candidate biomarkers should be evaluated for clinical efficacy.