|Year : 2018 | Volume
| Issue : 4 | Page : 480-483
Proteomic Analysis of the Serum of Patients with Stable Vitiligo and Progressive Vitiligo
Yi-Lei Li1, Hong Wang2, Rui-Qun Qi1, Yu-Xiao Hong1, Song Zheng1, Bi-Huan Xiao1, Qian An1, Jiu-Hong Li1, Hong-Duo Chen1, Xing-Hua Gao1
1 Department of Dermatology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
2 Department of Bioengineering, College of Life Science and Technology, Jinan University, Guangzhou, Guangdong 510632, China
|Date of Submission||26-Oct-2017|
|Date of Web Publication||09-Feb-2018|
Prof. Xing-Hua Gao
Department of Dermatology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001
Source of Support: None, Conflict of Interest: None
|How to cite this article:|
Li YL, Wang H, Qi RQ, Hong YX, Zheng S, Xiao BH, An Q, Li JH, Chen HD, Gao XH. Proteomic Analysis of the Serum of Patients with Stable Vitiligo and Progressive Vitiligo. Chin Med J 2018;131:480-3
|How to cite this URL:|
Li YL, Wang H, Qi RQ, Hong YX, Zheng S, Xiao BH, An Q, Li JH, Chen HD, Gao XH. Proteomic Analysis of the Serum of Patients with Stable Vitiligo and Progressive Vitiligo. Chin Med J [serial online] 2018 [cited 2018 Nov 16];131:480-3. Available from: http://www.cmj.org/text.asp?2018/131/4/480/225055
Yi-Lei Li and Hong Wang contributed equally to this work.
To the Editor: Vitiligo is not a fatal disease; however, it can have strong social and psychological impacts on the patients. Genetic susceptibility, autoimmunity, neural dysregulation, melanin self-destruction, and oxidative stress may be involved in the pathogenesis of vitiligo. Based on its clinical development, vitiligo can be divided into the progressive vitiligo (PV) or the stable vitiligo (SV). To date, at least 50 susceptible genes had been found in vitiligo. However, only a few genes presented a clear association with the high inherent risk of vitiligo and the protein levels of these genes were scarce. Recently, combination methods had been introduced in order to obtain better results. Nevertheless, no validated tool could confirm on the stage of vitiligo. Proteomics is a useful tool for large-scale screening of disease-related proteins. It could provide a better understanding of the biological and molecular events for this disease. Isobaric tag for relative and absolute quantitation (iTRAQ) is a systematic protein quantitative analytical method. It has the highest flux, the smallest system error, and the most powerful function for diagnosis, treatment, and prognosis. In this study, we identified differentially expressed proteins using iTRAQ-based proteomic technology and constructed an interaction network for SV and PV with the aim to identify potential group proteins as markers to distinguish between SV and PV.
All the patients and healthy controls were enrolled at the Dermatological Outpatient Clinic of theFirst Hospital of China Medical University from 2015 to 2016. Vitiligo was diagnosed clinically by experienced dermatologists. Staging of the disease was performed according to the Vitiligo Disease Activity Score (VIDA) in the Consensus of Vitiligo Diagnosis and Treatment (2014 edition) issued by the Dermatology Committee of Pigmentary Disease of the Chinese Association of Integrative Medicine. Patients with PV (VIDA score over 2 points) showed emergence of new skin lesions expansion of the original skin lesions or occurrence of the Koebner phenomenon within three months. Patients with SV were defined as those with stable lesions for at least one year. The sites and progression of the lesions and the extent of cutaneous involvement were documented.
Clinical information of the patients with PV and SV are presented in [Supplementary Table 1] [Additional file 1]and [Supplementary Table 2] [Additional file 2]. Ten serum samples from each group were pooled together for proteomic experiments. The remaining 20 serum samples were used in Western blotting experiments for verification of results.
Pooled serum samples from each group were prepared before proteomic analysis. The ProteoPrep Blue Albumin and IgG Depletion kit (Sigma-Aldrich, Co., St. Louis, MO, USA) were used to remove albumin, and IgG protein concentration was estimated using the BCA Protein Assay Kit (Thermo Scientific, Rockford, California, USA). Total proteins of each group were analyzed by iTRAQ (AB Sciex, Framingham, Massachusetts, USA), liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS). Protein samples (100 μg) were reduced, alkylated, and subjected to tryptic hydrolysis. iTRAQ labeling was performed using iTRAQ Reagents manufacturer's protocol. Each sample was labeled with the respective iTRAQ tags. All the labeled peptides were merged and evaporated to dryness in a vacuum centrifuge.
The iTRAQ labelled samples werefirstly diluted to 100 μl with H2O buffer (NH3•H2O, pH=10) before high performance liquid chromatography on a Dionex Ultimate 3000 system (Dionex, Sunnyvale, CA, USA) at 25°C on a Gemini NX 3u C18 110A; 150.0 mm × 2.0 mm Phenomenex column, and Gemini 3u C6 Phenyl 110A; 100.0 mm × 2.0 mm column (all from Phenomenex, Torrance, CA, USA). The flow rate used for reversed-phase column separation was 0.2 ml/min with H2O (mobile Phase A) and 80% ACN (mobile Phase B). A solvent gradient system was used: 0–15 min, 5–10% B; 15–48 min, 15–25% B; 48–60 min, 25–37% B; 60–65 min, 37–95% B; and 65–70 min, 95% B. The elution was monitored by absorbance at 214/280 nm, and fractions were collected every 50 s. In total, 10 fractions were combined and dried.
Peptides were separated by a linear gradient according to the manufacture's instruction. MS analysis was performed on a Q Exactive system (Thermo Fisher Scientific, California, USA) in information-dependent mode. MS spectra were acquired across the mass range of 350–1800 m/z in high-resolution mode (>35,000); a maximum of 20 precursors per cycle were chosen for fragmentation from each MS spectrum with a 120-ms minimum accumulation time for each precursor and dynamic exclusion for 10s. The tandem mass spectra were recorded in high-sensitivity mode (resolution >175,000) with rolling collision energy and iTRAQ reagent collision energy adjustments.
Proteins extracted from the serum samples of 20 patients with SV, 20 patients with PV, and 20 healthy controls were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (11–15% acrylamide). After transferring the blots onto PVDF membranes and blocking overnight (5% skim milk and 0.05% Tween-20 in PBS), the primary antibody was added for 1 h, followed by PBS washing, addition of a secondary HRP-conjugated antibody, and development with a chemiluminescence detection system (ECL, Pierce, USA). Anti-plasma serine protease inhibitor (SERPINA5), hepatocyte growth factor activator (HGFAC), anti-leucine zipper protein 4 (LUZP4), and anti-phosphoinositide phospholipase C (PLCH2) were used (Abcam, USA).
The interaction network of differentially expressed proteins was constructed by the Cytoscape (National Institute of General Medical Sciences, USA), which is an open source software platform for visualizing molecular interaction networks and biological pathways and integrating these networks with annotations. The differentially expressed proteins involved in biological processes, molecular functions, cell components, and pathway enrichment were evaluated using the Cytoscape platform based on Gene Ontology (GO) terms. The corresponding P value analysis was simultaneously obtained using ReactomeFIViz (National Institute of General Medical Sciences).
The peptide data were analyzed with Protein Pilot Software 5.0 using the Paragon protein database search algorithm (AB Sciex, Framingham, Massachusetts, USA). The resulting MS/MS spectra were searched against the International Protein Index human sequence database (version 3.83). The parameters for the analysis were set as follows: Cys alkylation with methyl methanethiosulfonate, digestion with trypsin, and allowance of up to one missed trypsin cleavage. The false discovery rate (FDR) analysis was also performed using the integrated tools (FDR ≤0.01).
All the data were analyzed using Statistical Package for the Social Sciences Software (SPSS Inc., Chicago, IL, USA) version 16.0. The significant difference was analyzed by one-way analysis of variance (ANOVA). The gray value ratio of each band compared between the groups (SV or PV compared to control) was used to calculate the significant difference by least significant difference as a post hoc test. A value of P < 0.05 was considered statistically significant.
In total, 171 differentially expressed proteins were identified through iTRAQ. Compared with the control group, there were 80 (42 upregulated and 38 downregulated) and 89 (56 upregulated and 33 downregulated) differentially expressed proteins in the SV and PV groups, respectively [Supplementary Supplementary Table 3] [Additional file 3] and [Supplementary Table 4] [Additional file 4]. Among these differentially expressed proteins, 39 showed similar changes in both the SV and PV groups. Among them, the differentially expressed upregulated and downregulated proteins were 19 and 20. When the progressive stage was compared with the stable stage of vitiligo, 71 (23 upregulated and 48 downregulated) proteins were found to be differentially expressed [Supplementary Table 5] [Additional file 5].
The differentially expressed proteins in the SV and PV groups were analyzed based on their GO clustering using the Cytoscape platform [Supplementary Figure 1] [Additional file 6] and [Supplementary Figure 2] [Additional file 7]. The significance of thefirst 15 annotated functions was ranked according to the P values. The differently expressed proteins were categorized based on their molecular function, biological process, pathway enrichment, and cell component.
In the SV group, molecular function of the differentially expressed proteins included immunoglobulin (Ig) receptor binding, antigen binding, phosphatidylcholine binding, serine-type endopeptidase inhibitor activity, endopeptidase inhibitor activity, Ig binding, protease binding, complement component C1q binding, serine-type endopeptidase activity, oxygen transporter activity, glycoprotein binding, heparin binding, proteoglycan binding, low-density lipoprotein particle binding, and laminin binding proteins, as compared to the controls. PV group expressed the above proteins similar to those in the SV group, except that had no differential expression of complement component C1q binding, proteoglycan binding, low-density lipoprotein particle binding, and laminin binding. In addition, hemoglobin binding, haptoglobin binding, arachidonic acid binding, Toll-like receptor 4 binding, peptidoglycan binding, and heme binding proteins were also differentially expressed in the PV group.
Enrichment in pathway showed that differentially expressed proteins for both the vitiligo stages were involved in complement cascade, clotting cascade, sequestering of ions, and reverse cholesterol transport. However, amb2 integrin signaling and retinoid metabolism pathway were only identified in the progressive stage. The differentially expressed proteins in the biological process were those involved in complement activation, B cell and phagocytosis recognition, and innate immune response of both the vitiligo groups. Differentially expressed proteins related to responses to bacterium and the acute-phase process were detected in PV. The differentially expressed proteins related to cell components included proteins involved in extracellular components such as exosomes, Ig complexes, and the related matrix, in both the vitiligo groups. Functional proteins involved in the acute-phase response, such as the sequestering of ions, reverse lipid transport, and oxygen transport, were markedly highly expressed in the PV group, indicating that these proteins and their molecular functions and pathways may play primary roles in the pathogenesis of vitiligo.
Based on the results obtained from the iTRAQ-based proteomic analyses, the four most prominent differentially expressed proteins were reexamined by Western blotting. The two proteins (SERPINA5 and HGFAC) were upregulated in both the stages of vitiligo. SERPINA5 expression was significantly different in both the stages of vitiligo (SV vs. control, P < 0.05; PV vs. control, P < 0.05). In addition, PLCH2 expression was upregulated in the SV (SV vs. control, P < 0.05) and LUZP4 was significantly downregulated in the PV (PV vs. control, P < 0.01) [Supplementary Figure 1].
To better understand the mechanism underlying the pathogenesis of vitiligo, a protein interaction network for the differentially expressed proteins identified in the SV and PV groups was constructed using Cytoscape. The proteins marked with circles and different colors were identified in our analysis, and those marked with boxes are the linker proteins added by the Cytoscape platform [Supplementary Figure 3] [Additional file 8].
Vitiligo is a common chronic acquired disease characterized by depigmentation. Currently, no specific curative therapy and no satisfactory method are available to predict or control the progression of the disease. Proteomics is a feasible approach for large-scale screening of vitiligo-related proteins to elucidate its pathogenesis. The present study employed iTRAQ-based quantitative proteomic tools to identify vitiligo-related proteins in the serum of patients with vitiligo. Disadvantage of this method is that the differentially expressed proteins identified might not fully represent the differentially expressed proteins in the independent sample. However, we generally choose the intersection of different proteins identified in the mixed samples and then selected individual samples to validate the proteomic results by Western blotting experiment. This method has also been proved to be reasonable and feasible in several reports. Our results revealed differentially expressed proteins in the vitiligo samples. Among them, 39 differentially expressed proteins were detected in both the vitiligo groups.
|Figure 1: The differential expression of four proteins in the different stages of vitiligo and controls. (a) Each column represents one group, and the groups were as follows: controls, patients with stable, and those with progressive patients. Beta-actin was used as the loading control. (b) The gray value ratio of each band compared between the groups (SV or PV compared with control) was used to calculate the significant differences by one-way analysis of variance. *Represents that the difference is statistically significant in PLCH2 (P < 0.05, SV vs. control); †represents that the difference is statistically significant in LUZP4 (P < 0.01, PV vs. control); ‡represents that the difference is statistically significant in SERPINA5 (P < 0.05, SV vs. control and PV vs. control). SV: Stable vitiligo; PV: Progressive vitiligo; HGFAC: Hepatocyte growth factor activator; LUZP4: Leucine zipper protein 4; PLCH2: Phosphoinositide phospholipase C; SERPINA5: Plasma serine protease inhibitor.|
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Autoimmunity is believed to be the primary cause of vitiligo. In our study, we identified many Ig heavy chain V proteins and Ig chain C proteins that were differentially expressed in SV and PV compared with the controls. We also identified the IgA complex significantly differentially expressed in both the stages of vitiligo compared to the controls from the cell component analysis. Therefore, the increased or decreased plasma levels of Ig heavy chain V or chain C might be potential group proteins requiring further investigation. Our proteomic analysis also showed increased levels of apolipoproteins (i.e., apolipoprotein A1, apolipoprotein A2, and apolipoprotein B) and decreased levels of serum paraoxonase/arylesterase 1 (PON1). These proteins were also analyzed to relate with lipid digestion, mobilization, and transport pathway. Pietrzak et al. reported that lipid metabolism was disrupted in vitiligo-affected children, possibly resulting from disrupted metabolic processes in the adipose tissue as well as oxidative stress. In our study, we found that PON1 levels were decreased in SV but not in PV. This finding indicates that PON1 may decrease when patients are in a stable condition.
Zinc-alpha-2-glycoprotein, an essential component of numerous proteins involved in biological defense mechanisms and functioning against oxidative stress, is differentially expressed in patients with SV. Some of these identified proteins are Zn (2+) dependent, such as the plasma protein histidine-rich glycoprotein. Thus, we propose that zinc ion-binding proteins may play a role in the pathogenesis of vitiligo. In addition to the proteins involved in zinc ion binding, some of the identified proteins were involved in calcium ion binding, such as PLCH2 and vitamin D-binding protein. The results of Western blotting test revealed that PLCH2 levels were increased in both the stages of vitiligo, especially in SV. These results showed that proteins with functions related to sequestering calcium ions and reverse cholesterol transport were expressed at markedly high levels in the PV group. There are reports in the literature that polymorphisms in the vitamin D receptor are associated with vitiligo. Clinical trials have also shown that the plasma levels of 25-hydroxy vitamin D and calcium are significantly decreased in patients with vitiligo. Ongoing studies continue to uncover potential roles for the components of the neurosensory system in the skin homeostasis and disease states.
In addition, interestingly, proteins involved in other pathways were identified and further verified through Western blotting. SERPINA5 protein, a negative regulator of the Toll pathway, was increased in both SV and PV. It seemed to be associated with micropapillary growth and the invasive phenotype of serious vitiligo that had protease inhibitor-independent activity. The expression and role of serine-type endopeptidase inhibitors in the differentiation of human skin pigmentation remains elusive. Some studies have identified a serine-type protease inhibitor related to palmitoyltransferase that has an effect on melanogenesis. Among other known serine protease inhibitors (SERPINs), the enhanced stability of PAI-1 might play a role in the development of autoimmune disease and the pathophysiology of vitiligo.
Moreover, another protein HGFAC was identified and validated in the serum of patients with vitiligo. This protein has serine-type endopeptidase activity and was found to play a role in malignant melanoma progression. Interestingly, LUZP4 levels were found to be decreased in both the vitiligo stages compared to those in the controls. Although LUZP4 was not associated with vitiligo, it has been frequently reported to be activated in melanoma, where it is required for growth. LUZP4 may function to promote the export of mRNAs, which would normally function to export proteins. Thus, it is possible that LUZP4 could also affect melanocyte cell growth.
In conclusion, our findings indicated that the autoimmunity proteins, lipid metabolism, oxidative stress proteins (Ig heavy chain V and C, HBB, HBG1, and HBA1), ion-dependent proteins (zinc-alpha-2-glycoprotein, PLCH2, and vitamin D-binding protein), and serine-type inhibitor proteins (increases in SERPINA5 and decreases in LUZP4) might be involved in the pathogenesis of vitiligo. Even though the sample size was small, the differentially expressed proteins that were identified might provide useful information for the diagnosis of early-stage vitiligo prior to the appearance of severe symptoms or for the elucidation of the pathophysiological mechanism.
Declaration of patient consent
We certify that we have obtained all appropriate patient consent forms. In the form, the patients have given their consent for their clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity.
Supplementary information is linked to the online version of the paper on the Chinese Medical Journal website.
Financial support and sponsorship
This work was supported by grants from the Public Welfare Program, Ministry of Health, China (No. 201202013), and the Innovative Research Team in Universities, Liaoning Bureau of Education (No. LT2012012).
Conflicts of interest
There are no conflicts of interest.
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