|Wei Liu1, Wen-xu Hong1, Yawen Zhang1, Xiaohu Ren1, Peimao Li2, Zhimin Zhang2, Haiyan Huang1, Xinfeng Huang1, Yanfang Zhang2* and Jianjun Liu1*|
|1Key Laboratory of Modern Toxicology of Shenzhen, Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, China|
|2Clinical Laboratory Department of Shenzhen hospital for Occupational Disease Treatment and Prevention, Shenzhen 518001, China|
|Corresponding Authors :||Jianjun Liu
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Shenzhen 518055, China
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|Received: October 15, 2015 Accepted: December 08, 2015 Published: December 20, 2015|
|Citation: Liu W, Hong Wx, Zhang Y, Ren X, Li P, et al. (2015) Dynamic Change of Serum Proteomics of Occupational Medicamentosa-like Dermatitis Induced by Trichloroethylene. J Clin Toxicol 5:275. doi:10.4172/2161-0495.1000275|
|Copyright: © 2015, Liu W, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.|
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Objective: Extensive researches about biomarkers of Occupational medicamentosa-like dermatitis induced by Trichloroethylene (OMLDT) have been carried out in recent years. But dynamic change of protein biomarkers in serum has rarely been reported. The aim of our study was to explore the dynamic changing law of serum proteins/ polypeptides in different periods of OMLDT, identify potential biomarkers and provide the scientific fundamentals for monitoring the progression of the disease and screening high-risk population.
Study design: We developed an approach in the combination of magnetic beads based weak cation exchange chromatography (MB-WCX), matrix assisted laser desorption ionization time of flight mass spectrometry (MALDITOF- MS) and ClinProTools software. Based on the alternations in the polypeptides fingerprint of serum (PFS), we built diagnostic models of OMLDT, and screened the special proteins/ polypeptides biomarkers and further studied the dynamic changing law of different periods in typical OMLDT patients.
Results: We attained 72 peaks which were statistical content in OMLDT/Normal model, of which 52 peaks were differential peaks. We also obtained 69 significant peaks in OMLDT/TCE Contact model, but the differential peaks were 35. There were 21 specific peaks which were alike among the differential peaks in these two models and the change of their expression level was consistent. Among the 21 specific peaks, we found 4 peaks, which were m/z 410942675065 and 9287Da, changed nearly the same in 3 periods of 4 recurrent patients, and 2 peaks (4109 and 9173 Da) changed consistent in 3 periods of 3 stable patients. Interestingly, m/z 4109 Da appeared in both groups and the expression level was increased with the course of disease. So it may be the special serum biomarker of OMLDT. Conclusion: Overall, the results indicate specific PFS could serve as a useful tool to reflect the dynamic change of proteins/ polypeptides in different periods of OMLDT. And it may provide a new clue for clinical application.