ISSN: 2161-0495
Journal of Clinical Toxicology
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Dynamic Change of Serum Proteomics of Occupational Medicamentosalike Dermatitis Induced by Trichloroethylene

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
8 Longyuan Road, Nanshan District
Shenzhen 518055, China
Tel: +86 0755 2550 8584
Fax: +86 0755 2550 8584
E-mail: bioresearch@hotmail.com; junii8@126.com
  Yanfang Zhang
70 Guiyuan North Road
Luohu District, Shenzhen 518001, China
Tel: +86 0755 61385573
E-mail: 13923431511@139.com
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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Abstract

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.

Abstract

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 (MALDI-TOF-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.

Keywords

Occupational Medicamentosa-like Dermatitis induced by Trichloroethylene; Serum; Proteomics; Biomarkers; Dynamic change

Introduction

Trichloroethylene (C2HCl3; TCE) is a major occupational hazard and environmental contaminant that can cause multisystem disorders such as occupational medicamentosa-like dermatitis. Occupational medicamentosa-like dermatitis induced by TCE (OMLDT) is dose-independent and potentially life threating disease. There is no obvious dose-effect relationship between the TCE exposure and incidence of OMLDT [1]. At present, the number of reported patients suffering from OMLDT in China has exceeded 500, and the mortality is about 9–13% [2]. OMLDT has become an important occupational health issue in China. But the traditional protective measures including personal protective devices couldn’t effectively reduce the incidence of OMLDT. One of the best ways to prevent the disease is to identify susceptible biomarkers used for screening employees before exposure and protecting susceptible populations.

At present, biomarker researches about OMLDT mainly focused on three aspects: gene polymorphism [3-7], serum proteomics [8-12] and TCE metabolites [13]. Haishan Li, et al. [3] identified genetic susceptible biomarkers associated with OMLDT in genes located in the human leukocyte antigen (HLA) region. Further research [4] had illustrated that HLA-B*13:01 was also identified in the patient of Japan. Later, they not only validated the association between HLA-B*13:01 allele and OMLDT, but also identified two new loci for the disease, one was on intron of the major histocompatibility complex class I chain related gene A (MICA), and another was between HLA-B and MICA, suggesting MICA was an important gene for the disease risk in addition to HLA-B*13:01. The combination of these single nucleotide polymorphisms (SNPs) could be an effective predicting biomarker for the disease among TCE-exposed populations [5]. Peiwu Huang, et al. [12] compared the expression of proteins in the sera of OMLDT patients at three different stages using 2D-DIGE and matrix-assisted laser desorption/ionization time-of- flight mass spectrometry (MALDI-TOF MS) strategy. They found that transthyretin (TTR), retinol binding protein 4 (RBP4) and haptoglobin could serve as potential serum biomarkers of OMLDT. Recently, Chloral hydrate (CH) seemed to be the culprit causative compound of OMLDT and the CH patch test could be potentially useful for the diagnosis of OMLDT and identification of subjects at risk of OMLDT [13].

Evidence existed that many disease processes were associated with quantitative and functional changes in the proteins of body fluids [14-16]. Serum was an easily accessible body fluid that contained different types of proteins released by various diseased tissues. Proteome analysis of the serum had played a central role in clinical diagnosis and monitoring and provided insight into disease pathophysiology and mechanisms. In our previous work [10,11], we built disease model of OMLDT by MALDI-TOF MS and ClinProTools bioinformatics software and further identified two proteins, ATP-binding cassette transporter family A member 12 (ABCA12) and cationic trypsinogen (PRSS1), by a liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS) approach. These findings would be helpful for clinical diagnosis, and providing novel scientific clue for mechanisms studies of OMLDT. However, to date, the dynamic change of serum proteomics about OMLDT has rarely been explored. In the present study, to elucidate and understand the dynamic changing law in serum of different periods of OMLDT patients, we compared the alternations in the polypeptides fingerprint of serum (PFS) between OMLDT/Contact model and OMLDT/Normal model by the methods built in our previous researches. We further studied the peak areas probability distribution of specific peaks and validated in different periods of seven typical OMLDT patients. Finally, we found a characteristic peak (m/z 4109 Da) which may be the special serum biomarker of OMLDT (Figure 1). This study could appropriately reflect the dynamic change of serum proteins/polypeptides in different periods of OMLDT. And it may provide a new clue for clinical application.

Materials and Methods

Clinical specimens

Eighteen OMLDT patients were enrolled in the study and 29 professional TCE contact people and 29 normal subjects were selected on an age-matched basis (± 3 years). The samples were allocated to the training set and the test set for model establishment and validation, respectively. Table 1 shows the basic clinical information of the three groups. The skin manifestations of the OMLDT patients enrolled in the study and the details for serum collection were described in our previous study [10,11]. The clinical manifestations included fever, rash, liver damage and so on. Approximately 4 ml of blood was collected for each person and centrifuged at 4 for 10 min to collect serum for MS analysis. Sera were collected by the Shenzhen hospital for Occupational Disease Treatment and Prevention after informed written consents were obtained. This study conformed to the ethical guidelines of the Declaration of Helsinki (World Medical Association, 1997) and was approved by the Institutional Review Board of this hospital.

Refer to the diagnostic criteria for occupational medicamentosa-like dermatitis induced by trichloroethylene (GBZ 185-2006) as defined by the Ministry of Health, the People's Republic of China, we further identified five periods of OMLDT: acute periodexfoliation periodbounce periodfade period and normal period or healed period according to patients conditionskin rashes characteristics and clinical examination indexes. Among eighteen OMLDT patients, seven patients with typical symptoms composed of 5 males and 2 females with a median age of 27 years (range 14-43 years) were used for dynamic change research. They were divided into two groups (Table 2). Group one contained four patients, whose condition were recurrent. The sera of exfoliation period, bounce period and fade period were collected and analyzed. Group two included three stable patients, who executed the criteria that the symptoms were in line with changes in the basic process of OMLDT, such as acute period, exfoliation period and healed period. The sera of these three periods were collected and analyzed.

Mass spectrometry and data interpretation

Peptidome separation of the serum samples and linear MALDI-TOF-MS analysis were described in our previous work [10,11]. Spectra had been collected automatically using the AutoXecuteTM software (Bruker Daltonics) for fuzzy-controlled adjustment of critical instrument settings to generate raw data of optimized quality. The ClinProToolsTM software (Version 2.2; Bruker Daltonics) had been used for all data interpretation steps, which started with a raw data pretreatment, including normalization of a set of spectra derived from a patient cohort, internal signal alignment using prominent internal signal peaks and a peak picking procedure. The signal-to-noise (S/N) ratio was higher than 5.0. Peak calculation was used areas. The whole data pretreatment has been completed using default settings and was performed automatically, without any user interaction.

ClinProTools offered three basic workflows: Peak Statistic Calculation, Model Generation and Classification. Peak Statistic Calculation included spectra recalibration and average spectra calculation, peak picking and peak calculation as well as peak statistic calculation. Model Generation included spectra recalibration and average spectra calculation, peak calculation and model generation based on the selected classification algorithm. Classification can be used to quickly classify test spectra with an existing model. This workflow included selecting the model to use and the spectra to classify, data preparation of these spectra according to the settings saved in the model and their classification. In this study, differential peaks were selected on the basis of statistical differences between relative protein/peptide ion peak areas with OMLDT and professional TCE contacts or healthy controls. These calculations were performed independently for peak areas. The difference of peak average areas (DAve) was larger; the separate power was stronger. The generally accepted value was more than 10. To characterize the peptidomes of OMLDT patients, we focused our attention on peaks where the DAve was larger than 11. The pretreated data were used to build models that could discriminate OMLDT patients from TCE contacts or normal subjects by a Supervised Neural Network (SNN) Algorithm. Model-building was performed by selecting a small subset of relevant peaks and establishing clusters using the peak areas. The accuracy of models were evaluated and validated by the test data.

Dynamic change research of serum proteomics for OMLDT

Through analyzing the differential peaks between the acute/contact model and the acute/normal model by the statistic methods, we figure out the specific peaks of OMLDT. To understand the dynamic changing law of different periods in the whole course and screen the special proteins/ polypeptides markers, we analyzed the dynamic change of PFS in different periods of 7 typical patients according to the specific peaks. The peak areas probability distribution could effectively reflect the expression level of serum proteins/polypeptides in some period. Single peak area divided by all peak areas was the peak areas probability. The dynamic change of serum proteins/polypeptides expression level could be observed by comparing the peak areas probability distribution in different periods. We calculated the peak areas probability distribution of specific peaks in different periods of each patient and validated in different periods of seven typical OMLDT patients.

Statistical analysis

ClinProTools supports various statistical tests and methods, including t-test, ANOVA test, Wilcoxon test, Kruskal-Wallis test and Anderson-Darling test. The Anderson-Darling (AD) test in the case of ClinProTools has been adapted to test for normal distributions. Each of these tests calculates the so-called p-value, which is the probability that an observed effect is simply due to chance. Accordingly, the lower the P value, the better a respective peak signal is suited to be used to separate the two classes. The generally accepted limit of the P values to consider a result significant is defined as 0.05 (0.01 for highly significant results). Hence, at first we should evaluate the p-value for the AD test. If it is above e.g., 0.05, we should consider the t-test or ANOVA test; otherwise, the result from Wilcoxon/Kruskal-Wallis (W/KW) test has to be evaluated. These calculations were done independently for peak areas.

Results

OMLDT/Normal model

ClinProTools provides a list of peaks sorted according to the statistical significance to differentiate between both classes. As a result, we got 158 peaks in OMLDT/Normal model, of which 72 peaks were statistical content (Supplementary Figures 1 and 2). There were 52 differential peaks which were significantly different in the 72 peaks (Table 3). Among these, 28 peaks were low expressed and 24 peaks were high expressed in OMLDT sera compared with those in healthy controls. We built the model by SNN algorithm. Three peptide ion signatures (m/z 2106.3, 2134.5 and 3263.67) were provided as class predictors for a cross-validation set, discriminating OMLDT samples from healthy controls (Supplementary Figures 3 and 4). Its cross validation and recognition capability were 94.44% and 100%, respectively. And the sensitivity and specificity were 100% and 66.7%, respectively (Table 4).

OMLDT/TCE Contact model

We attained 157 peaks in the training data set of OMLDT/TCE Contact model, of which 69 peaks were statistically significant (Supplementary Figures 1 and 2). There were 35 differential peaks in the 69 peaks (Table 5). Among the 35 peaks, 13 peaks were down-regulated and 22 peaks were up-regulated in OMLDT groups compared with those in TCE Contact controls. We also built the model by SNN algorithm, which consisted of 1450.33, 1866.16, 3262.39, 4109.55, 5064.85, 5248.05, 5956.57and 6667.04Da. Its cross validation and recognition capability were 97.22% and 100%, respectively. And the sensitivity and specificity were 81.3% and 100%, respectively, which explained good separating capacity (Table 4).

Results of specific peaks

To find some specific signals for OMLDT, we compared the differential peaks between the OMLDT/Normal model and the OMLDT/TCE contact model (Supplementary Figures 1 and 4). Twenty-one peaks were found in both models and the change of expression level was consistent (Tables 3 and 5). They were likely to be the specific peaks of OMLDT. Among them, nine were lower expressed and twelve were high expressed in the comparison of TCE contact group and Normal group. These specific peaks could be especially well visualized in the mass region among 1000 to 1800 Da, 2900 to 4300 Da, 5000 to 6000 Da, and 8800 to 9500 Da.

Dynamic change research for OMLDT

OMLDT specific peaks were further selected for analyzing and validating in seven typical cases. We studied the dynamic change of specific peaks in different periods by calculating peak area probability distribution. Seven patients were divided into two groups: recurrent group (group one, four cases) and non-recurrent group or stable group (group two, three cases) according to the disease situation. The dynamic change law of specific peaks in two groups was observed respectively.

Among 21 specific peaks, we found 4 peaks, which were m/z 4109 Da, 4267 Da, 5065 Da and 9287 Da, changed nearly the same in 3 periods of 4 recurrent patients (Figures 2A-2D). The expression change of m/z 4109 Da and 5065 Da presented the overall upward trend. The expression level of m/z 4267 Da presented the upward trend in No.2, 3 and 4 whereas downward trend in No.1 patient. The expression level of m/z 9287 Da presented an increasing trend in bounce period and then decreasing tendency in fade period of No.1, 3 and 4. But it was overall downward trend in No.2 patient. We then found the expression change rule of two specific peaks (m/z 4109 Da and 9173 Da) in three OMLDT cases among the acute period, exfoliation period and recovery period was exactly the same (Figures 2E and 2F). The expression levels of m/z 4109 Da showed an increasing trend, but the expression levels of m/z 9173 Da appeared a decreasing trend in exfoliation phase and then an increased trend in the recovery phase. 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.

Discussion

Mass spectrometry-based search for biomarker patterns was widely recognized as a valuable research tool for predictive medicine and pharmacological monitoring [17-20]. ClinProTools was the data interpretation software of the mass spectrometry-based ClinProtTM solutions for biomarker analysis [21]. The ClinProTools software was used for the data interpretation of MALDI-TOF spectra derived from serum samples of different groups. In this study, we had used two different control groups, namely TCE contact controls and healthy controls, which enabled us to distinguish general disease marker candidates from those that were specific for the OMLDT disease. We found that sera from OMLDT patients can be distinguished from healthy controls based on an array of 52 discriminated proteins/peptides. Most of them were consistent with our previous work [10]. On this basis, we further compared the differential peaks between OMLDT and TCE contact model. Thirty-five peaks were found and also in accordance with our preliminary study [11].

Within clinical proteomics, we were in general confronted with a small set of samples and a large number of identified peaks. The identification of biomarker candidates within ClinProTools focuses on the detected peaks over a given set of spectra. Peak area probability is a good indicator and rarely report in researches. The area of a peak is obtained by integrating the intensities over the region of the peak according to the full intensity values. Peak areas have a smaller variation between spectra than intensities of single points have. In our work, we calculated the peak area probability of thirty-five specific peaks in different periods of seven OMLDT patients and observed the change trend to find out the potential law. Because of differences in individual cases or may be complexity of patients condition changes [13,22,23], peak area probability distribution most of specific peaks had no obvious law. Among 21 specific peaks, we found 4 peaks, which were 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. We were exciting to discover m/z 4109Da peak appeared both in bounce group and non-bouncing group, and its expression level changed with the disease progression showed increasing tendency. Therefore, we initially considered m/z 4109Da specific peak may be OMLDT unique biomarker.

To date, however, there are still no available biomarkers for predicting who is at high risk of developing occupational medicamentosa-like dermatitis among TCE-exposed workers in clinical application. There were also some limitations and disadvantages in our study. Firstly, the sample size was not enough. A larger data set was needed for such a confirmation. Secondly, potential biomarker peaks should be selected for in-depth analysis after further enrichment to reduce the sample complexity and identified by tandem mass spectrometry (TOF/TOF MS). Moreover, identification via TOF/TOF fragment analysis should be performed to provide biological relevance to the statistical analysis.

Conclusively, ClinProTools was a very powerful software tool for the data interpretation in the field of clinical proteomics. It combined efficient visualization with automated data pretreatment and intuitive statistical analysis. In the present study, we had demonstrated that disease-specific proteomic fingerprints were present in the sera of OMLDT patients. Peak area probability distribution could well reflect the dynamic change law of polypeptides fingerprint in serum in the development of disease. These results provided scientific clues for further identification and crowd verification.

Acknowledgment

This work was supported by the NSFC (the National Natural Science Foundation of China) [81273126], the project of Guangdong science and technology plan [2013B031800001], the Project of Shenzhen Basic Research Plan [JCYJ20130329103949656, JCYJ20150402102135497] and the Future Industry Special Project of Shenzhen (ZDSYS20140509101335476).

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