High-throughput proteomic analysis of candidate biomarker changes in gingival crevicular fluid after treatment of chronic periodontitis.
High-throughput proteomic analysis of candidate biomarker changes in gingival crevicular fluid after treatment of chronic periodontitis.
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DOI：
10.1111/jre.12575
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发表时间：
2018-10
影响因子：
3.5
通讯作者：
Floudas CA
中科院分区：
文献类型：
--
作者：
Guzman YA;Sakellari D;Papadimitriou K;Floudas CA
无针对性、高通量的蛋白质组学方法在帮助识别牙周病诊断的生物标记物方面具有巨大的潜力。这些方法在寻找解决牙周治疗后牙周炎症的候选生物标志物方面的应用已经被研究。收集10例慢性牙周炎患者机械牙周治疗前、治疗后1、5、9、13周的龈沟液标本。分别于基线和13周记录牙周疾病的临床指标，包括探诊深度、探诊后退缩、临床附着水平和探诊时出血情况。样品用在线液相色谱-纳米电喷雾-混合离子陷阱-轨道捕获质谱仪进行分析。用Pilot_Protein蛋白质组学软件处理光谱。治疗13周后临床指标明显改善(Wilcoxon Sign RANSING检验，p<；0.05)。从大量识别的蛋白质中，通过时间模式匹配、Logistic函数拟合和混合整数线性优化等过滤方法提取出一小部分蛋白质。这一亚组包括天青素、溶菌酶C和肌球蛋白-9作为候选生物标记物，在治疗13周后突出，在基线和α-平滑肌肌动蛋白显著。交叉验证研究得出的平均预测准确率和曲线下面积分别为0.900和0.930。高通量蛋白质组学分析有助于确定牙周治疗的终点。这些候选生物标记物应该进行临床疗效评估。
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. 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. Clinical parameters were significantly improved 13 weeks after treatment (Wilcoxon signed ranks test, p< 0.05). 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. High-throughput proteomic analysis can contribute to identifying endpoints of periodontal therapy. These candidate biomarkers should be evaluated for clinical efficacy.
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影响因子：
3.4
作者：
Deutsch, Eric W.;Mendoza, Luis;Shteynberg, David;Farrah, Terry;Lam, Henry;Tasman, Natalie;Sun, Zhi;Nilsson, Erik;Pratt, Brian;Prazen, Bryan;Eng, Jimmy K.;Martin, Daniel B.;Nesvizhskii, Alexey I.;Aebersold, Ruedi
通讯作者：
Aebersold, Ruedi
影响因子：
6.7
作者：
Berglundh, Tord;Zitzmann, Nicola U.;Donati, Mauro
通讯作者：
Donati, Mauro
影响因子：
4.4
作者：
DiMaggio, Peter A., Jr.;Floudas, Christodoulos A.;Yates, John R., III
通讯作者：
Yates, John R., III
影响因子：
--
作者：
Cherkassky, V
通讯作者：
Cherkassky, V
影响因子：
4.4
作者：
Bostanci, Nagihan;Ramberg, Per;Papapanou, Panos N.
通讯作者：
Papapanou, Panos N.