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Research Article

Label-Free Proteomics Reveals Decreased Expression of CD18 and AKNA in Peripheral CD4+ T Cells from Patients with Vogt-Koyanagi-Harada Syndrome

  • Liming Mao,

    Affiliation: Laboratory of Ophthalmology, Chongqing Eye Institute, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China

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  • Peizeng Yang mail,

    peizengycmu@126.com

    Affiliation: Laboratory of Ophthalmology, Chongqing Eye Institute, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China

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  • Shengping Hou,

    Affiliation: Laboratory of Ophthalmology, Chongqing Eye Institute, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China

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  • Fuzhen Li,

    Affiliation: Laboratory of Ophthalmology, Chongqing Eye Institute, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China

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  • Aize Kijlstra

    Affiliation: The Department of Ophthalmology, University of Maastricht, Maastricht, The Netherlands

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  • Published: January 28, 2011
  • DOI: 10.1371/journal.pone.0014616
Corrections

4 Mar 2011: Mao L, Yang P, Hou S, Li F, Kijlstra A (2011) Correction: Label-Free Proteomics Reveals Decreased Expression of CD18 and AKNA in Peripheral CD4+ T Cells from Patients with Vogt-Koyanagi-Harada Syndrome. PLoS ONE 6(3): 10.1371/annotation/2c8e3e74-556c-4e42-a5df-0b8ce294119f. doi: 10.1371/annotation/2c8e3e74-556c-4e42-a5df-0b8ce294119f | View correction

Abstract

Vogt-Koyanagi-Harada (VKH) syndrome is a systemic autoimmune disease. CD4+ T cells have been shown to be involved in autoimmune diseases including VKH syndrome. To screen aberrantly expressed membrane proteins in CD4+ T cell from patients with active VKH syndrome, blood samples were taken from five patients with active VKH syndrome and five healthy individuals. A label-free quantitative proteomic strategy was used to identify the differently expressed proteins between the two groups. The results revealed that the expression of 102 peptides was significantly altered (p<0.05) between two groups and matched amino acid sequences of proteins deposited in the international protein index (ipi.HUMAN.v3.36.fasta). The identified peptides corresponded to 64 proteins, in which 30 showed more than a 1.5-fold difference between the two groups. The decreased expression of CD18 and AKNA transcription factor (AKNA), both being three-fold lower than controls in expression identified by the label-free method, was further confirmed in an additional group of five active VKH patients and six normal individuals using the Western blot technique. A significantly decreased expression of CD18 and AKNA suggests a role for both proteins in the pathogenesis of this syndrome.

Introduction

Vogt-Koyanagi-Harada (VKH) syndrome is an autoimmune disorder mainly affecting systemic melanocytes including those in the eyes, meninges, ears, skin, and hair [1]. It is one of the most common uveitis entities in China as well as in the Far East of Asia. The uveitis seen in this syndrome is mostly characterized by a chronic granulomatous inflammation with recurrent episodes [2]. Although the mechanisms involved in VKH syndrome are not fully elucidated, previous reports have showed that CD4+ T cells sensitive to melanocytes are responsible for the development of VKH syndrome [3], [4]. Recent studies have shown that molecules related to CD4+ T cell function, including Fas/FasL [5], [6], T-bet [7], IFN-γ, RORγt and IL-17 [7], [8], are involved in the pathogenesis of this syndrome. As CD4+ T cells exert their role principally through a variety of receptors, adhesion molecules,transport proteins and costimulatory molecules,which have been found mainly in the plasma membrane or endomembrane systems, our study focused on the differentially expressed proteins in the CD4+ T cell membrane of VKH patients.

Proteomics provides important tools for identifying molecules involved in both normal and pathological processes [9], [10], [11], [12], [13], [14], [15]. A recently established label-free strategy [16], [17] has been shown to offer a more robust protein identification and quantitation, easy automation and large-scale analysis with greater efficiency as compared to conventional two dimensional electrophoresis (2-DE) approaches [18], [19], [20], [21]. In the present study, we investigated the differentially expressed membrane proteins in active VKH patients using this label-free proteomic method. Western blot technique was used to validate the proteomic results. Our results showed a significantly decreased expression of CD18 and AKNA in CD4+ T cells from patients with active VKH syndrome.

Materials and Methods

Clinical Samples

Ten active VKH patients and eleven healthy individuals were included in the present study. CD4+ T cells from five active VKH patients and five healthy individuals were used for label-free proteomics analysis. CD4+ T cells from another group of five active VKH patients and six controls were used for a validation study.The diagnosis of VKH syndrome was made according to the criteria revised for VKH syndrome by an international nomenclature committee [2]. The patients included in our study had not been systemically treated with any immunosuppressive agent for at least one week prior to blood sampling. The mainly clinical features of VKH patients were shown in Table 1. The healthy individuals are sex and age matched with VKH patients showing not any past history of infectious or chronic diseases. An informed consent (written) was obtained from all patients and the procedures had been approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University.

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Table 1. The clinical features of the investigated patients with VKH disease.

doi:10.1371/journal.pone.0014616.t001

CD4+ T Cell isolation and flow cytometry

The PBMCs were prepared from heparinized blood by Ficoll-Hypaque density-gradient centrifugation and were rinsed for three times in phosphate-buffered saline (PBS). Peripheral CD4+ T cells were purified using human CD4 microbeads according to the manufacturer's instructions (Miltenyi Biotec, Palo Alto, Calif). Briefly, PBMCs were suspended in 80 µl of PBS containing 0.5% bovine serum albumin (BSA) and 2 mM ethylenediamine tetraacetic acid (EDTA) per 107 total cells. A volume of 20 µl of CD4 microbeads was added to this suspension and incubation was performed for 15 min at 4°C. The cells were then washed in 2 ml of PBS containing 0.5% BSA and 2 mM EDTA and applied to a magnetic column on a MiniMACS separation unit (Miltenyi Biotec, Palo Alto, Calif). The CD4+ T cell fraction was collected and used in subsequent experiments. For flow cytometric analysis, aliquots of 1×106 PBMCs and isolated CD4+ T cells were stained with PE-cy7-conjugated monoclonal antibody (mAb) against human CD4 and appropriate isotype controls (eBioscience, San Diego, CA, USA) for 30 min at 4°C in the dark. Flow cytometric analysis was performed using FACS Calibur and CellQuest software (BD Biosciences, SanJose, CA).

Membrane Protein Preparation

Membrane proteins were extracted using a membrane protein extraction kit (Merck KGaA, Darmstadt, Germany) according to the manufacturer's instructions. Briefly, 5×106 CD4+ T cells were pelleted by centrifugation at 300 g for five min. The supernatant was carefully removed and discarded. Reagent A (150 µl) was added to the cell pellet and a homogeneous cell suspension was prepared by pipetting up and down and was then incubated for 10 min at room temperature with occasional vortexing. The lysed cells were placed on ice. One part of reagent B with two parts of reagent C were mixed. A total amount of 450 µl of the mixed reagent B and C was added to each tube of lysed cells and then vortexed. The tubes were incubated on ice for 30 min with occasional vortexing. After centrifuging at 10,000 g for 3 min at 4°C, the supernatant was transferred to a new tube and was incubated for 10 min at 37°C to separate the membrane protein fraction. The tubes were then centrifuged at room temperature for 2 min at 10,000 g to isolate the hydrophobic fraction from the hydrophilic fraction. The hydrophilic phase (top layer) was removed carefully from the membrane-enriched phase (bottom layer). The protein concentration of the hydrophobic protein was determined using the protein assay kit (Bio-Rad, Hercules, CA). The protein was aliquoted and stored at −80°C until use.

Protein Digestion

Protein digestion was performed as follows. The protein concentration was adjusted to 5 ug/ul with lysis buffer. The aforementioned membrane protein was chemically reduced for 2.5 h at room temperature by adding DTT to 10 mM, and then carboxyamidomethylated in 50 mM iodoacetamide for 40 min at room temperature in the dark. Endoprotease LysC (Roche, Indianapolis, IN) was added to a final substrate: enzyme ratio of 100:1 (w/w), and the reaction was incubated at 37° for 3 h. The urea concentration in protein samples was adjusted to 1.5 M with 25 mM NH4HCO3, and then 2 µg of trypsin was added to a final substrate: enzyme ratio of 50:1 (w/w). The trypsin digest was incubated at 37° for 20 h. The peptide mixture was acidified by formic acid to 0.1% for further MS analysis.

Analysis by HPLC and MS

Separation of the trypsin-digested peptides was performed with the Ettan MDLC system (GE Healthcare, Piscataway, NJ). Peptide samples were first desalted through a Zorbax 300SB-C18 peptide traps (Agilent Technologies, Wilmington, DE) and then separated by a 0.15 mm*150 mm (RP-C18) column (Column Technology Inc, Fremont, CA) with micro spray mode. An aqueous 0.1% formic acid solution was used as phase A and a solution of 0.1% formic acid and 84% acetonitrile was used as phase B. The peptides were separated with a linear gradient of 4%~50% mobile phase B over 2 hours at a flow rate of 1300 nl/min. The column temperature was maintained at 170 °C.Both pooled samples were analyzed in triplicate.

The separated peptides were analyzed on a Finnigan LTQ linear ion trap mass spectrometer (Thermo Finnigan, San Jose, CA) equipped with a nanoelectrospray ion source [16], [17]. The injection voltage was set to 3.0 kV and the normalized collision energy was set to 35.0. The number of ions stored in the ion trap was regulated by the automatic gain control. Each scan cycle consisted of one full scan mass spectrum (m/z 0~1600) collected in profile mode. Five MS/MS events were recorded in centroid mode with the dynamic exclusion settings: repeat count 2, repeat duration 30 s, exclusion duration 90 s. MS/MS spectra were searched against the IPI human protein database (version 3.36; 69012 entries) and analyzed using SEQUEST of BioworksBrowser rev. 3.1. The search was performed with one missed cleavage permitted. The fixed and variable modifications were set to Carbamidomethyl (C) and oxidation (M) respectively. The mass tolerance for precursor ions was set to 3.0 Da. The mass tolerance for fragment ions was set to 1.0 Da. The peptide matches were filtered based on cross-correlation scores (Xcorr) of 1.9, 2.2 and 3.75 for charge states 1+, 2+, and 3+, respectively. All the peptides with different charges were translated to single charge and expressed as “MH+” (Supplemental Table 1). To determine the false discovery rate (FDR), the data set was searched against a sequence-reversed decoy IPI human version 3.36 database using the same search parameters. FDR was calculated using the formula reported by Xue et al [17]: FDR = Number of false peptides/(Number of true peptides+Number of false peptides)×100%.

Label-free Detection and Quantitation of Peptides

The peaklist of peptides was generated using DeCyder MS software version 1.0 (GE Healthcare, Piscataway, NJ) and the quantitative analysis of peptides was performed. Peptide detection, elution profile comparison, background subtraction and peptide quantitation were carried out on the full scan precursor mass spectra in fully automatic mode. Peptide quantitation is based on MS signal intensities of individual LC-MS analyses. Different signal intensity maps were matched using the pepmatch module and then the peptide quantitative results were acquired. As there was no internal standard, the intensity distributions for all peptides detected in both samples were used for normalization. Throughout these studies the mass tolerance in the software was set to 0.5 atomic mass unit and the retention time tolerance was set to 2 min.

Western blot analysis

Total protein of CD4+ T cells from five active VKH patients and six healthy individuals were prepared for Western blot analysis. Ten micrograms of protein from each specimen were used for SDS-PAGE. The gels were then transferred to a PVDF membrane. Membranes were incubated with antibodies at dilutions of 1:1000 for both anti-human CD18 mAb (R&D systems, Minneapolis, MN) and anti-human AKNA mAb (Genway Biotech, San Diego, CA). Proteins were detected using the Phototope-HRP western blot detection system (Cell Signaling, Danvers, MA).

Statistical analysis

For differential analysis of peptides from both VKH patients and normal controls, student's t-test was performed using the Decyder MS software. A p-value of less than 0.05 was considered as a significant difference. In the validation study, the immunoblot bands were quantified using Bio-rad quantity one 1-D analysis software. Band density was normalized to that of an internal control, β-actin. The differences of immunoblot band intensities between both groups were analyzed by student's t-test using SPSS software (version 13.0). A p-value of less than 0.05 was considered as statistically significant.

Results

CD4+ T cell isolation

CD4+ T cells were sorted from peripheral blood of ten active VKH patients and eleven healthy individuals using CD4 microbeads. The purity of CD4+ T cells identified in both patients and controls was 98% (Figure 1). The obtained CD4+ T cells from both groups were subjected to label-free quantitative proteomic analysis followed by Western blot validation.

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Figure 1. Representative flow cytometric map of CD4+ T cells isolated from peripheral blood of active VKH patients.

The PBMC and freshly isolated CD4+ T cells were stained with the indicated markers using fluorescence-labeled mAb and analyzed by flow cytometry. Before sorting, the ratio of CD4+ T cells in PBMC was 38.11% (A). After microbeads based sorting, the purity of CD4+ T cells was as high as 98.06% (B).

doi:10.1371/journal.pone.0014616.g001

Data quality evaluation

Before conducting the relative protein profiling analysis between the two pooled samples from VKH patients and normal controls, several quality control measures were performed on the replicates of both pools to determine analytical reproducibility. The mass precision of the extracted peptide components was within 5 parts per million (ppm) of mass error. The quality control analysis revealed similar results between patients and controls concerning the median and average mass errors (1.90 vs 2.80 ppm), intensity errors (2.10% vs 1.95%), and retention time (0.70% vs 0.50%).

Reproducibility between duplicate runs was evaluated using a binary comparison map. The horizontal (x) axis represents the distribution of peak intensities of the first run, and the vertical (y) axis represents that of the second run. The average intensity correlation coefficient (CC) between the two runs was 0.92. A representative binary comparison map is shown in Figure S1. The expected distribution of the duplicate runs showed no obvious change. These quality control measurements were performed on all runs and both experimental pools.

Relative quantitation of peptides in total membrane protein extracts

The digested membrane proteins of CD4+ T cells prepared from active VKH patients and normal controls were analyzed by LC-MS/MS. Base peak ion chromatograms were acquired in profile mode to get enough data points for evaluation by DeCyder MS. A representative base peak ion chromatogram from the analysis of the VKH pool is shown in Figure S2. The other runs displayed similar profiles. The MS files were converted to signal intensity maps, 2-D representations with m/z on the y axis and retention time on the x axis. A representative intensity map (CD18) is shown in Figure 2, A and B. The density of the grayscale pattern was proportional to the signal intensity of a peptide peak at a certain m/z, retention time, and charge state. The signal intensity maps were analyzed using the PepDetect module of DeCyder MS to detect and quantitate the peptide ions. Since each sample was analyzed in triplicate, peptides from six signal intensity maps were matched and analyzed. As the peptide content reflects the protein level, relative quantitation for corresponding proteins was obtained after importing identification results. A p-value of 0.05 was used as a threshold to identify significantly up- or down-regulated expression. The MS/MS data was collected in centroid mode and was transferred to TurboSEQUEST software for protein identification.

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Figure 2. Decyder MS intensity graphs of CD18 derived from LC-MS/MS analysis of a total membrane protein extracted from both normal group (A) and VKH group (B).

The location of peptide ALNEITESGR in both samples was labeled with square frames in the total graphs and the magnified graphs (C and D). Statistical analysis showed that the different expression of peptide ALNEITESGR was significant between VKH group and normal group (E).

doi:10.1371/journal.pone.0014616.g002

In total 354 peptides were identified to be significantly different between both groups, in which 102 could be sequenced and which corresponded to 64 proteins. A FDR of 3.77% at the peptide level was obtained by searching against the sequence-reversed decoy IPI human database, suggesting a high fidelity of this strategy. Thirty proteins showed a difference of at least 1.5-fold, when comparing active VKH patients with normal controls. Of these, eight proteins were identified based on two or more unique peptides (Table 2) and in 22 the identification was based on a single peptide (Table 3). All the identified peptides and their sequences of each protein in both samples are listed in the Table S1. For single-peptide-based identifications, the MS/MS spectra appropriately labeled were listed in Figure S3.

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Table 2. The differently expressed proteins identified based on two or more peptides in CD4+ T cells between active VKH patients and normal individuals.

doi:10.1371/journal.pone.0014616.t002
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Table 3. The differently expressed proteins identified based on single peptides in CD4+ T cells between active VKH patients and normal individuals.

doi:10.1371/journal.pone.0014616.t003

Validation of CD18 and AKNA by Western blot technique

Among the thirty proteins identified, two proteins, CD18 and AKNA, were found to have a more than three-fold difference between active VKH patients and normal controls. The different expression of CD18 in both samples was shown in magnified intensity graphs (Figure 2, C and D). Our study further validated the expression of these two proteins using the Western blot technique. The immunoreactive band intensities were quantitated and were presented as intensity volumes (vol %). The results showed that the expression of CD18 was about five-fold lower in the active VKH patients as compared to normal controls. As for the expression of AKNA, a similar result was observed (Figure 3). The results for both proteins were generally consistent with the quantitative data of the label-free method.

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Figure 3. Validation of CD18 and AKNA by the Western blot technique.

Antibodies were used at a dilution of 1:1000 for the anti-human CD18 monoclonal antibody and anti-human AKNA monoclonal antibody. Proteins were detected using the Phototope-HRP Western blot detection system(A). The immunoreactive band intensities were quantitated and were presented as intensity volumes (vol%). The results showed that both CD18 and AKNA were significantly down-regulated in VKH patients as compared to normal controls (B). VKH: VKH patients, NC: normal controls.

doi:10.1371/journal.pone.0014616.g003

Discussion

In this study, label-free relative quantitative proteomics was employed to screen differently expressed membrane proteins in CD4+ T cells from active VKH patients and normal controls. Thirty proteins were identified to be at least 1.5 fold differently expressed between active VKH patients and normal individuals. A significantly decreased expression of CD18 and AKNA was further confirmed using the Western blot technique. The results presented here may provide new clues for the study on molecules involved in the pathogenesis of VKH syndrome.

CD4+ T cells have been shown to be crucial in the pathogenesis of this syndrome [5], [6], [22]. The present study aimed at searching for the differently expressed proteins in these cells between active VKH patients and normal controls. Proteomics has provided useful strategies for the screening of differently expressed proteins. The recently established label-free strategy has been proven to be more efficient as compared to conventional 2-DE approaches [18], [19], [20], [21]. It has been used to identify urinary biomarkers from antineutrophil cytoplasmic antibodies [23] and differently expressed proteins from patients with ductal carcinoma [24] and Parkinson's disease [25]. These studies have proven that the label-free strategy is a powerful tool for identifying disease-associated proteins. In this study, we employed the label-free approach to identify differently expressed proteins in CD4+ T cells, an important population involved in various autoimmune diseases, between active VKH patients and normal controls.

To ensure the accuracy and stability of the label-free quantitative analysis, several procedures were performed. For the purpose of minimizing the effect of individual differences on the experimental results, an equal amount of membrane proteins of CD4+ T cells from five active VKH patients and five normal controls were pooled respectively and subjected to the label-free quantitative proteomics. For label-free proteomics, strategies using three or four technical replicates have been reported [16], [17], [26]. As three technical replicates are a minimal requirement for statistical analysis and have already been justified by a number of studies, we chose three technical replicates in the present study. Our results showed a great reproducibility in peptide quantitation (Table S1). To acquire stable MS data needed for quantitative analysis, several quality control measures were used [16]. The data from each run of both VKH patients and normal controls appeared to be reproducible in terms of mass accuracy, retention time and intensity. Binary comparison maps used in the experiment validated the reproducibility between duplicates. Coefficient of variation (CV) analysis [17] was performed and the result showed that the average CV of the fold changes was 23% for proteins with two or more peptides, which ensured the accuracy of protein quantitation (Table S2).

Our label-free proteomic results showed that thirty proteins were different in expression for more than 1.5 fold between active VKH patients and normal controls,although these results need to be confirmed by other techniques. By searching for the functions of these proteins according to annotations in the Swiss-prot database at http://expasy.org, we interestingly noted that these identified proteins are involved in 54 biological processes (Table S3) and 39 functional groups (Table S4). Importantly, we found that a number of molecules, including vimentin, dystrophin, Keratin 10, AKNA and CD18, are associated with an inflammatory and/or immune response. An abnormal expression of certain proteins has also been found to be involved in autoimmune diseases or immune related diseases [27], [28], [29]. Enhanced vimentin immunoreactivity was found in the spinal cord of rats with experimental autoimmune encephalomyelitis [28]. Increased expression of dystrophin was observed in experimental autoimmune myasthenia gravis [27].

By analyzing the data acquired using the label-free method, it was interesting to note that a number of proteins identified in this study, including vimentin and AKNA transcription factor, were non-membrane components. A similar result has also been observed in proteomic studies. These non-membrane molecules could be explained as being a component associated with the membrane or with membrane fractions [30], [31]. It is worthy to point out that the increased expression of keratins may be involved in the pathogenesis of a number of diseases affecting the skin [32], [33], [34], [35]. It is not clear whether keratins are truly induced and, if it is, how it functions in VKH syndrome, an autoimmune disease frequently affecting the integumentary system.

False positive data always exist in proteomic studies. Therefore, the proteomic results usually need to be confirmed using other techniques. As protein with a big different expression ratio is readily confirmed by another technique, we chose two proteins with a three-fold different expression between VKH patients and normal controls, CD18 and AKNA, as candidates in the Westernblot identification. As expected, the Western blot result revealed a five-fold lower expression of both proteins in active VKH patients as compared with normal controls. It is worthwhile to point out that both CD18 and AKNA were identified using the one peptide based identification, as already reported previously [36], [37]. This could be due to the lower abundance of both proteins in the tested samples.

CD18, also known as integrin beta 2 or ITGB2, is one of the members of the integrin family, which is involved in cell adhesion, neutrophil chemotaxis, and cell-surface mediated signaling. The expression of CD18 can be down-regulated by introducing an insertion mutation in the murine CD18 gene. Due to this mutation, a chronic inflammatory skin disease clinically resembling human psoriasis develops in PL/J mice [38], [39]. It has been reported that resistance of T cells to apoptosis may be involved in the pathogenesis or perpetuation of VKH syndrome [5], [6]. CD18 has been shown to be required in apoptosis of human T cells [40], [41], [42]. A decreased expression of CD18 in active VKH patients may suggest that an insufficient inhibition on apoptosis of CD4+ T cells is present at the active stage.

ANKA, also known as AHCTF1, is reported to regulate the expression of CD40 and CD40L [43], both molecules essential in immune and inflammatory responses. The interaction of CD40 and CD40L has been shown to be involved in the negative regulation of T cell autoreactivity and abnormalities in their interaction may lead to autoimmunity. In the present study, a significantly decreased expression of AKNA was noted in CD4+ T cells from active VKH patients. The decreased AKNA may be presumed to down-regulate the expression of CD40L in CD4+ T cells. Such a decreased expression of CD40L has indeed been observed in peripheral CD4+ T cells in autoimmune thyroid disease [44]. More studies are needed to clarify whether a decreased AKNA could play a role in VKH syndrome via down-regulating CD40 and CD40L.

In summary, label-free quantitative proteomics showed abnormal expression of thirty proteins for more than 1.5 fold in active VKH patients. A significantly decreased expression of CD18 and AKNA was confirmed using the Western blot technique. Both proteins may be involved in the pathogenesis of VKH syndrome. However, our study did not elucidate the mechanism by which both proteins exert their function. Further studies about the effect of both proteins on cytokine expression, apoptosis and chemotaxis of CD4+ T cells will contribute to our understanding about the mechanisms in the pathogenesis of VKH syndrome.

Supporting Information

Figure S1.

A representative binary comparison map of duplicate runs of samples from VKH patients. The horizontal (x) axis represents the distribution of peak intensities of the first run, and the vertical (y) axis represents that of the second run. The average intensity correlation coefficient (CC) between the two runs was 0.92. The expected distribution of the duplicate runs showed no obvious change.

doi:10.1371/journal.pone.0014616.s001

(0.04 MB PDF)

Figure S2.

A representative base peak ion chromatogram from LC-MS/MS analysis of a total membrane protein extract from the VKH group digested with trypsin.

doi:10.1371/journal.pone.0014616.s002

(0.19 MB PDF)

Figure S3.

The MS/MS spectra for single-peptide-based identifications. The detected b and y ions used in the protein identification were labeled in the MS/MS spectra. The unlabeled peaks are a, c, x or z ions generated in mass spectrometer.

doi:10.1371/journal.pone.0014616.s003

(1.88 MB PDF)

Table S1.

All the identified peptides and their sequences of each protein in both samples.

doi:10.1371/journal.pone.0014616.s004

(0.08 MB XLS)

Table S2.

C.V.analysis of all the identified proteins based on at least two peptides.The average CV of the fold changes was 23% for proteins with two or more peptides.

doi:10.1371/journal.pone.0014616.s005

(0.06 MB XLS)

Table S3.

Biological Process of the identified proteins.

doi:10.1371/journal.pone.0014616.s006

(0.02 MB XLS)

Table S4.

The molecular function of the identified proteins.

doi:10.1371/journal.pone.0014616.s007

(0.08 MB XLS)

Author Contributions

Conceived and designed the experiments: PY. Performed the experiments: LM. Analyzed the data: LM SH. Contributed reagents/materials/analysis tools: FL. Wrote the paper: LM PY AK.

References

  1. 1. Sigle KJ, McLellan GJ, Haynes JS, Myers RK, Betts DM (2006) Unilateral uveitis in a dog with uveodermatologic syndrome. J Am Vet Med Assoc 228: 543–548.
  2. 2. Read RW, Holland GN, Rao NA, Tabbara KF, Ohno S, et al. (2001) Revised diagnostic criteria for Vogt-Koyanagi-Harada disease: report of an international committee on nomenclature. Am J Ophthalmol 131: 647–652.
  3. 3. Yamaki K, Gocho K, Sakuragi S (2002) Pathogenesis of Vogt-Koyanagi-Harada disease. Int Ophthalmol Clin 42: 13–23.
  4. 4. Yamaki K, Kondo I, Nakamura H, Miyano M, Konno S, et al. (2000) Ocular and extraocular inflammation induced by immunization of tyrosinase related protein 1 and 2 in Lewis rats. Exp Eye Res 71: 361–369.
  5. 5. Yang P, Chen L, Zhou H, Zhong H, Wang H, et al. (2002) Resistance of lymphocytes to Fas-mediated apoptosis in Behcet's disease and Vogt-Koyangi-Harada syndrome. Ocul Immunol Inflamm 10: 47–52.
  6. 6. Yang P, Ji L, Zhou H, Huang X, Xie C, et al. (2001) Disturbed expression of Fas/FasL on CD4(+) and CD8(+)T cells in Behcet's disease, Vogt-Koyanagi-Harada syndrome, and idiopathic anterior uveitis. Ocul Immunol Inflamm 9: 185–191.
  7. 7. Li B, Yang P, Zhou H, Huang X, Jin H, et al. (2005) Upregulation of T-bet expression in peripheral blood mononuclear cells during Vogt-Koyanagi-Harada disease. Br J Ophthalmol 89: 1410–1412.
  8. 8. Liu X, Yang P, Lin X, Ren X, Zhou H, et al. (2009) Inhibitory effect of Cyclosporin A and corticosteroids on the production of IFN-gamma and IL-17 by T cells in Vogt-Koyanagi-Harada syndrome. Clin Immunol 131: 333–342.
  9. 9. Chambers G, Lawrie L, Cash P, Murray GI (2000) Proteomics: a new approach to the study of disease. J Pathol 192: 280–288.
  10. 10. Kavallaris M, Marshall GM (2005) Proteomics and disease: opportunities and challenges. Med J Aust 182: 575–579.
  11. 11. Kraemer KH (2004) From proteomics to disease. Nat Genet 36: 677–678.
  12. 12. McGregor E, Dunn MJ (2003) Proteomics of heart disease. Hum Mol Genet 12 Spec No 2: R135–144.
  13. 13. Wright GL Jr, Semmes OJ (2003) Proteomics in Health and Disease. J Biomed Biotechnol 2003: 215–216.
  14. 14. Hanash S (2003) Disease proteomics. Nature 422: 226–232.
  15. 15. Jungblut PR, Zimny-Arndt U, Zeindl-Eberhart E, Stulik J, Koupilova K, et al. (1999) Proteomics in human disease: cancer, heart and infectious diseases. Electrophoresis 20: 2100–2110.
  16. 16. Johansson C, Samskog J, Sundstrom L, Wadensten H, Bjorkesten L, et al. (2006) Differential expression analysis of Escherichia coli proteins using a novel software for relative quantitation of LC-MS/MS data. Proteomics 6: 4475–4485.
  17. 17. Xue H, Lu B, Zhang J, Wu M, Huang Q, et al. (2010) Identification of serum biomarkers for colorectal cancer metastasis using a differential secretome approach. J Proteome Res 9: 545–555.
  18. 18. Rabilloud T (2002) Two-dimensional gel electrophoresis in proteomics: old, old fashioned, but it still climbs up the mountains. Proteomics 2: 3–10.
  19. 19. Klein E, Klein JB, Thongboonkerd V (2004) Two-dimensional gel electrophoresis: a fundamental tool for expression proteomics studies. Contrib Nephrol 141: 25–39.
  20. 20. Friedman DB (2007) Quantitative proteomics for two-dimensional gels using difference gel electrophoresis. Methods Mol Biol 367: 219–239.
  21. 21. Gorg A, Weiss W, Dunn MJ (2004) Current two-dimensional electrophoresis technology for proteomics. Proteomics 4: 3665–3685.
  22. 22. Ohta K, Yoshimura N (2002) Bcl-2 expression by CD4 T lymphocytes in Vogt-Koyanagi-Harada disease. Ocul Immunol Inflamm 10: 93–103.
  23. 23. Quintana LF, Campistol JM, Alcolea MP, Banon-Maneus E, Sol-Gonzalez A, et al. (2009) Application of label-free quantitative peptidomics for the identification of urinary biomarkers of kidney chronic allograft dysfunction. Mol Cell Proteomics 8: 1658–1673.
  24. 24. Hu X, Zhang Y, Zhang A, Li Y, Zhu Z, et al. (2009) Comparative serum proteome analysis of human lymph node negative/positive invasive ductal carcinoma of the breast and benign breast disease controls via label-free semiquantitative shotgun technology. Omics 13: 291–300.
  25. 25. Xun Z, Kaufman TC, Clemmer DE (2009) Stable Isotope Labeling and Label-Free Proteomics of Drosophila parkin Null Mutants. J Proteome Res 8: 4500–4510.
  26. 26. Gillardon F, Kloss A, Berg M, Neumann M, Mechtler K, et al. (2007) The 20S proteasome isolated from Alzheimer's disease brain shows post-translational modifications but unchanged proteolytic activity. J Neurochem 101: 1483–1490.
  27. 27. Asher O, Fuchs S, Zuk D, Rapaport D, Buonanno A (1992) Changes in the expression of mRNAs for myogenic factors and other muscle-specific proteins in experimental autoimmune myasthenia gravis. FEBS Lett 299: 15–18.
  28. 28. Shin TK, Lee YD, Sim KB (2003) Embryonic intermediate filaments, nestin and vimentin, expression in the spinal cords of rats with experimental autoimmune encephalomyelitis. J Vet Sci 4: 9–13.
  29. 29. Ghosh S, Seward R, Costello CE, Stollar BD, Huber BT (2006) Autoantibodies from synovial lesions in chronic, antibiotic treatment-resistant Lyme arthritis bind cytokeratin-10. J Immunol 177: 2486–2494.
  30. 30. Zhang L, Xie J, Wang X, Liu X, Tang X, et al. (2005) Proteomic analysis of mouse liver plasma membrane: use of differential extraction to enrich hydrophobic membrane proteins. Proteomics 5: 4510–4524.
  31. 31. Le Bihan T, Goh T, Stewart II, Salter AM, Bukhman YV, et al. (2006) Differential analysis of membrane proteins in mouse fore- and hindbrain using a label-free approach. J Proteome Res 5: 2701–2710.
  32. 32. Corden LD, McLean WH (1996) Human keratin diseases: hereditary fragility of specific epithelial tissues. Exp Dermatol 5: 297–307.
  33. 33. Smith F (2003) The molecular genetics of keratin disorders. Am J Clin Dermatol 4: 347–364.
  34. 34. Irvine AD, McLean WH (1999) Human keratin diseases: the increasing spectrum of disease and subtlety of the phenotype-genotype correlation. Br J Dermatol 140: 815–828.
  35. 35. Ku NO, Gish R, Wright TL, Omary MB (2001) Keratin 8 mutations in patients with cryptogenic liver disease. N Engl J Med 344: 1580–1587.
  36. 36. Elias JE, Gygi SP (2007) Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods 4: 207–214.
  37. 37. Xu H, Yang L, Wang W, Shi SR, Liu C, et al. (2008) Antigen retrieval for proteomic characterization of formalin-fixed and paraffin-embedded tissues. J Proteome Res 7: 1098–1108.
  38. 38. Wilson RW, Ballantyne CM, Smith CW, Montgomery C, Bradley A, et al. (1993) Gene targeting yields a CD18-mutant mouse for study of inflammation. J Immunol 151: 1571–1578.
  39. 39. Wang H, Peters T, Sindrilaru A, Scharffetter-Kochanek K (2009) Key role of macrophages in the pathogenesis of CD18 hypomorphic murine model of psoriasis. J Invest Dermatol 129: 1100–1114.
  40. 40. Larsson J, Serrander L, Stendahl O, Lundqvist-Gustafsson H (2000) Involvement of the beta2-integrin CD18 in apoptosis signal transduction in human neutrophils. Inflamm Res 49: 452–459.
  41. 41. Walzog B, Jeblonski F, Zakrzewicz A, Gaehtgens P (1997) Beta2 integrins (CD11/CD18) promote apoptosis of human neutrophils. Faseb J 11: 1177–1186.
  42. 42. Wu MX, Ao Z, Hegen M, Morimoto C, Schlossman SF (1996) Requirement of Fas(CD95), CD45, and CD11a/CD18 in monocyte-dependent apoptosis of human T cells. J Immunol 157: 707–713.
  43. 43. Siddiqa A, Sims-Mourtada JC, Guzman-Rojas L, Rangel R, Guret C, et al. (2001) Regulation of CD40 and CD40 ligand by the AT-hook transcription factor AKNA. Nature 410: 383–387.
  44. 44. Watanabe M, Yamamoto N, Matsuzuka F, Miyauchi A, Iwatani Y (2004) Decrease of CD154 intensity on peripheral CD4+ T cells in autoimmune thyroid disease. Clin Exp Immunol 136: 555–558.