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Cornea  |   June 2015
iTRAQ-Based Quantitative Proteomic Analysis of Tear Fluid in a Rat Penetrating Keratoplasty Model With Acute Corneal Allograft Rejection
Author Affiliations & Notes
  • Feifei Huang
    Key Laboratory of Myopia Ministry of Health, Department of Ophthalmology, Eye, Ear, Nose and Throat Hospital, Fudan University, Shanghai, China
  • Jianjiang Xu
    Key Laboratory of Myopia Ministry of Health, Department of Ophthalmology, Eye, Ear, Nose and Throat Hospital, Fudan University, Shanghai, China
  • Hong Jin
    Institutes of Biomedical Sciences, Fudan University, Shanghai, China
  • Jianwen Tan
    Key Laboratory of Myopia Ministry of Health, Department of Ophthalmology, Eye, Ear, Nose and Throat Hospital, Fudan University, Shanghai, China
  • Chaoran Zhang
    Key Laboratory of Myopia Ministry of Health, Department of Ophthalmology, Eye, Ear, Nose and Throat Hospital, Fudan University, Shanghai, China
  • Correspondence: Chaoran Zhang, No.83 Fenyang Road, Xuhui District, Shanghai, China; fdeent@126.com
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 4117-4124. doi:10.1167/iovs.14-16207
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      Feifei Huang, Jianjiang Xu, Hong Jin, Jianwen Tan, Chaoran Zhang; iTRAQ-Based Quantitative Proteomic Analysis of Tear Fluid in a Rat Penetrating Keratoplasty Model With Acute Corneal Allograft Rejection. Invest. Ophthalmol. Vis. Sci. 2015;56(6):4117-4124. doi: 10.1167/iovs.14-16207.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose.: This study aimed to develop a greater understanding of the mechanisms underlying acute corneal allograft rejection by identifying differentially expressed tear proteins at defined stages and discovering potentially important proteins involved in the process.

Methods.: The isobaric tags for relative and absolute quantitation (iTRAQ)-two-dimensional liquid chromatography-tandem mass spectrometry (2DLC-MS/MS) technique was used to identify tear proteins showing significant alterations in a rat penetrating keratoplasty model at different time points. Bioinformatics technology was applied to analyze the significant proteins, and a potential protein was verified by Western blotting.

Results.: A total of 269 proteins were quantified, and 118 proteins were considered to be significantly altered by at least 2.0- or 0.5-fold. For gene ontology annotations, the top enrichments were neurological disease, free radical scavenging, cell death and survival, and cell movement. For pathway analyses, the top enrichments were LXR/RXR activation, acute phase response signaling, clathrin-mediated endocytosis signaling, and coagulation system. Coronin-1A was verified as a potential protein involved in the early stage of acute corneal allograft rejection.

Conclusions.: This study first demonstrates that tear proteomics is a powerful tool for better understanding of the mechanisms underlying acute corneal rejection, and that coronin-1A in tears might be closely related to allograft rejection.

Corneal blindness is second only to cataracts as a cause of blindness worldwide, and corneal transplantation remains the main method of visual rehabilitation. Despite improved therapies after penetrating keratoplasty (PKP), corneal graft rejection is still the leading cause of graft failure, with an incidence rate of 30%.1,2 A recent study observed that the total 5- and 10-year graft survival rates after corneal transplantation were 74% and 64%, respectively, and that the estimated predicted graft survival rates were 27% at 20 years and only 2% at 30 years.3 Although intensive studies on the precise mechanisms of graft rejection have been conducted, there is still no overall understanding, and there are no biomarkers that ophthalmologists can use to monitor allograft rejection. 
Tears comprise a complex fluid mixture of proteins, lipids, salts, mucin, and other small organic molecules.4 Although the minimally invasive nature of sample collection methods using glass microcapillary tubes or Schirmer strips makes tear fluid a very interesting option for proteomic studies, such analyses may be challenging because of the small volume of fluid that can be collected.5 Many previous proteomic studies have provided evidence of tangible alterations to the protein expression profiles in dry eye disease, such as the upregulation of α-enolase, α-1-acid glycoprotein 1, S100A8, S100A9, S100A4, and S100A11, and the downregulation of lipocalin-1, lysozyme, prolactin-inducible proteins (PIP), and lactoferrin.6,7 In a rabbit model of Sjögren syndrome, S100A6, S100A9, serum albumin, serotransferrin, PIP, polymeric immunoglobulin receptor, and Ig gamma chain C region were found to be altered using isobaric tags for relative and absolute quantitation (iTRAQ).8 In addition, lacrimal proline-rich protein 4 was considered as a potential biomarker for dry eye syndrome.9 Alterations in the tear proteome have been the subject of several other ocular surface studies on keratoconus10,11 and pterygium.12 However, to date, there have been no tear proteomic studies on acute corneal allograft rejection. 
Tear proteomic studies have been carried out using a wide range of quantitative techniques, including stable isotope labeling with amino acids in cell culture,13 isotope codes affinity tag,14 nonisobaric tags for relative and absolute quantitation,15 and iTRAQ. Compared with the other proteomic approaches, iTRAQ can greatly increase the number of identified proteins, as it does not discriminate against peptides with certain physicochemical properties, unlike the other proteomic technologies.16 
In this study, we focused our efforts on the dynamic alterations to the tear proteome in a rat PKP model for further understanding of the mechanisms underlying allograft rejection. We used an iTRAQ-based quantitative proteomic approach both to identify differentially expressed proteins in tears at defined stages and to discover potentially important proteins involved in corneal rejection. 
Materials and Methods
Animals
Male Wistar rats (7 weeks of age, weighing 200–250 g) and male Sprague-Dawley (SD) rats (6 weeks of age, weighing 200–250 g) were used in this study. All of the rats were obtained from the Animal Experimental Center of Fudan University. Ethical approval was obtained before commencement of the study from the Council on Animal Care and the Animal Use Subcommittee at our hospital. The animals were cared for in accordance with ARVO Statement for the Use of Animals in Ophthalmic and Vision Research. 
Corneal Transplantation
The 42 Wistar rats were divided into three groups (14 rats per group). For the allograft group (Wistar–SD), Wistar rats were the recipients and SD rats were the donors. For the autograft group (Wistar–Wistar; traumatic control group), the central cornea (5.5-mm diameter) of the right eye was excised, and then replaced onto the same corneal bed and secured. No intervening measures were performed for the blank control group. 
Orthotopic corneal transplantation was performed on the right eye as reported previously.17 Briefly, the central cornea (5.5-mm diameter) was excised from a donor rat using a corneal trephine and placed on Optisol (Chiron Vision, Irvine, CA, USA). The graft bed was prepared by excising a 5.0-mm site in the central cornea of the recipient rat. The donor button was then placed onto the recipient bed and secured with eight interrupted 10-0 nylon sutures. Graft survival was evaluated on days 1, 2, 7, and 14 after transplantation. 
Sample Collection
The experimental design is shown in Figure 1A. For the allograft and autograft groups, tears from both eyes were collected at three time points. For the blank control group, tears were only collected from the right eye on day 7. With the rats under general anesthesia, tears were collected from the lower fornices using a 10-μL glass microcapillary tube and then expelled into an Eppendorf tube. The tear volume of individual rats was 7 to 10 μL. All samples were stored at −80°C until processing. 
Figure 1
 
Experimental design. (A) Entire experimental design. (B) iTRAQ design.
Figure 1
 
Experimental design. (A) Entire experimental design. (B) iTRAQ design.
Clinical Evaluation
Animals with transplanted eyes showing intraoperative or immediate postsurgical complications before day 2 were excluded and replaced with the next grafted animal on the randomization schedule.18 Graft rejection was scored (0–4) based on the degree of corneal edema, transparency, and neovascularization, as reported previously.19 Graft rejection was defined as a total score of 4 or more. Rejection scores for all experimental grafts were assessed on days 2, 7, and 14 after transplantation. 
Removal of High-Abundance Proteins
The seven pooled tear specimens for iTRAQ were subjected to high-abundance protein depletion. The 14 most abundant proteins (albumin, IgG, antitrypsin, IgA, transferrin, haptoglobin, fibrinogen, α2-macroglobulin, α1-acid glycoprotein, IgM, apolipoprotein AI, apolipoprotein AII, complement C3, and transthyretin) were removed using a Removal System Affinity Column (Agilent Technologies, Santa Clara, CA, USA) according to the manufacturer's instructions. Following depletion, the samples were concentrated using 5-kDa-cutoff ultracentrifugation columns. The total protein concentrations were determined by the bicinchoninic acid assay. 
iTRAQ Labeling and Two-Dimensional Liquid Chromatography-Tandem Mass Spectrometry (2DLC-MS/MS)
Only the pooled samples from the right eye were examined. The proteins in the samples were precipitated with isopropanol, and the pellets were redissolved in dissolution buffer (0.5 M triethylammonium bicarbonate, 0.1% SDS). The proteins were then reduced, alkylated, digested, and labeled with iTRAQ reagents (Applied Biosystems, Foster City, CA, USA) as shown in Figure 1B. The labeled peptides were mixed, desalted with Pep-Pak Cartridges (Waters, Milford, MA, USA), and fractionated using a Shimadzu UFLC System (Shimadzu, Tokyo, Japan) connected to a strong cation exchange column (PolySULFOETHYL Column; 2.1 mm × 100 mm, 5 μL, 200 A; The Nest Group, Inc., Southborough, MA, USA). Strong cation exchange separation was performed using a linear binary gradient of 5% to 25% buffer B (10 mM KH2PO4, 350 mM KCl, 25% ACN, pH 2.6) in buffer A (10 mM KH2PO4, 25% ACN, pH 2.6) at a flow rate of 200 μL/min for 60 minutes. Twenty fractions were collected, centrifuged in a rotation vacuum concentrator (Christ RVC 2-25; Christ, Osterode, Germany), and redissolved in buffer C (5% ACN, 0.1% formic acid solution). The individual fractions were analyzed using a QSTAR XL LC/MS/MS System (Applied Biosystems) and a reserve-phase liquid chromatography (RPLC) column (ZORBAX 300SB-C18 Column; 5 μm, 300 A, 0.1 × 15 mm; Microm, Auburn, CA, USA). The RPLC gradient was 5% to 35% buffer D (95% acetonitrile, 0.1% formic acid) in buffer C at a flow rate of 0.3 μL/min for 120 minutes. Survey scans were acquired from m/z 400 to 1800 with up to four precursors selected for MS/MS from m/z 100 to 2000. 
Database Search
All MS/MS samples were analyzed using Mascot (version 2.4.1; Matrix Science, London, UK). Mascot was set up to search the SwissProt_2013_02 database (selected for Rattus, 2013.02, 7854 entries) assuming trypsin as the digestion enzyme. Mascot searched with a fragment ion mass tolerance of 0.60 Da and a parent ion tolerance of 400 PPM. Methylthiolation of cysteine and iTRAQ8plex of lysine and the N-terminus were specified in Mascot as fixed modifications. Oxidation of methionine, acetylation of the N-terminus, and iTRAQ8plex of tyrosine were specified in Mascot as variable modifications. 
Criteria for Protein Identification
Scaffold (version 4.0.5; Proteome Software, Inc., Portland, OR, USA) was used to validate the MS/MS-based peptide and protein identifications. Protein identifications were accepted if they could be established at more than 50% probability and contained at least one peptide identified by the Peptide Prophet algorithm. Peptide identifications were established at more than 95% probability. Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. Acquired intensities in the experiment were normalized globally across all acquisition runs. All normalization calculations were performed using medians to normalize data multiplicatively. The criteria for significant proteins were fold changes of more than 2.0 or less than 0.5. 
Hierarchical Clustering Analysis
Hierarchical clustering of significant proteins based on the above criteria was used to visualize the relative quantitative protein changes in both groups at three time points, using Cluster 3.0 (Human Genome Center, University of Tokyo, Japan) and Java TreeView software (SunMicrosystems, Santa Clara, CA, USA). The clustering parameters were iteratively determined to be correlation (uncentered) metrics and complete linkage methods using the normalized mean Log2 ratios for each protein. 
Gene Ontology (GO) Annotation and Protein Pathway Analysis
Gene ontology annotations and pathway analyses of the identified significant proteins were obtained using Ingenuity Pathway Analysis (Ingenuity Systems, Inc., Redwood City, CA, USA; see www.ingenuity.com). Overrepresented functions and pathways were generated based on information contained in the Ingenuity Pathways Knowledge Base. 
Western Blotting
The expressions of coronin-1A in tears from both eyes were validated by Western blotting. For each gel, 20 μg protein was loaded. Briefly, tear samples were separated by 10% SDS-PAGE and electrophoretically transferred to polyvinylidene difluoride membranes (Millipore, Bedford, MA, USA) using a mini transblot apparatus (Bio-Rad Laboratories, Hercules, CA, USA). After blocking with PBS/0.05% Tween-20 containing 5% nonfat dry milk for 1 hour, the membranes were incubated with a primary anti-coronin-1A antibody (1:5000; Abcam, Cambridge, MA, USA) overnight at 4°C, followed by incubation with a horseradish peroxidase–conjugated secondary antibody (1:10,000; Abcam) for 1 hour at room temperature. We evaluated β-actin as a control for protein loading, and it was detected using a mouse monoclonal anti-β-actin antibody (1:3000; Sigma-Aldrich Corp., St. Louis, MO, USA). The antibody–antigen complexes were visualized using an enhanced chemiluminescence agent (GE Healthcare Biosciences, Maidstone, UK), and densitometric analysis of the protein bands was performed using Quantity One software (Bio-Rad Laboratories). 
Statistical Analysis
Statistical analyses were carried out using Statistical Package for Social Sciences software (SPSS 19.0 for windows; IBM SPSS Statistics, IBM Corporation, Chicago, IL, USA), and values of P less than 0.05 were considered statistically significant. Data were presented as mean ± SD. An unpaired Student's t-test was used for clinical manifestation comparisons between different groups at the same time point. 
Results
Clinical Evaluation
The rejection scores in the allograft group were significantly higher than those in the autograft group. The allograft group scores increased steadily, and by day 14, all remaining grafts had a score of 2 or more and the rejection index (RI) was greater than 7 (Table). 
Table
 
Clinical Evaluation of Rat PKP Model (Mean ± SD)
Table
 
Clinical Evaluation of Rat PKP Model (Mean ± SD)
Proteins Identified by iTRAQ
A total of 269 differentially expressed proteins and 2712 spectra were identified by iTRAQ in the tears of the Wistar rat corneal transplantation model. The identified proteins were filtered with selected filter exclusion parameters (fold changes of >2 or <0.5), and 118 proteins were screened out as significant proteins compared with the controls for at least one time point (Supplementary Table S1). 
Hierarchical Clustering of the Significant Proteins
The hierarchical clustering analysis of the 118 differentially expressed proteins was visualized in a heat map format (Fig. 2). During the rejection process after allograft corneal transplantation, the protein patterns on days 2 and 7 were more similar than the pattern on day 14. In the autograft group (traumatic control group), the protein patterns on days 7 and 14 were more similar than the pattern on day 2. There were significant differences in protein expression between the allograft group and the autograft group. The default settings of the software were used in the analyses, thus ensuring confidence of the data. 
Figure 2
 
Hierarchical cluster analysis of proteins identified in this study. Green signifies downregulated proteins and red signifies upregulated proteins. The clustering shows that in the allograft group, the expression patterns at day 7 are more similar to those at day 2 than to those at day 14; whereas in the autograft group (traumatic control group), the expression patterns at day 7 are more similar to those at day 14 than to those at day 2. Overall, the expression patterns between the allograft group and the autograft group are quite different. Coronin-1A is featured to show its expression changes at different time points in both the allograft and autograft groups.
Figure 2
 
Hierarchical cluster analysis of proteins identified in this study. Green signifies downregulated proteins and red signifies upregulated proteins. The clustering shows that in the allograft group, the expression patterns at day 7 are more similar to those at day 2 than to those at day 14; whereas in the autograft group (traumatic control group), the expression patterns at day 7 are more similar to those at day 14 than to those at day 2. Overall, the expression patterns between the allograft group and the autograft group are quite different. Coronin-1A is featured to show its expression changes at different time points in both the allograft and autograft groups.
Gene Ontology Annotation and Pathway Analysis
To better understand the biological functions of the differentially expressed significant proteins, both GO annotations and pathway analyses were performed. The top four enrichment functions were neurological disease, free radical scavenging, cell death and survival, and cell movement (Supplementary Fig. S1). The top four enrichment pathways were LXR/RXR activation, acute phase response signaling, clathrin-mediated endocytosis signaling, and coagulation system (Supplementary Fig. S1). 
Potential Protein Candidate
Coronin-1A was selected as a candidate biomarker for acute corneal rejection. Compared with the autograft group (traumatic control group), the iTRAQ results showed a sharply increased expression level of coronin-1A on day 7 in the allograft group that remained high on day 14 (Fig. 3A). Representative peptide MS/MS spectra are shown in Figures 3B and 3C. 
Figure 3
 
Identification and relative quantification of coronin-1A using iTRAQ. (A) The histograms indicate the Log2 fold changes in coronin-1A between the allograft transplantation group, autologous keratoplasty group (traumatic control group), and blank control group. Higher bars mean larger fold changes. (B) Sequences containing the DAGPLLISLK peptide, which forms coronin-1A. The histograms indicate the normalized Log2 intensity of each labeling channel, which reflects the peptide abundance in each sample. (C) Another identified peptide sequence comprising KSDLFQEDLYPPTAGPDPALTAEEWLSGR, leading to the identification of coronin-1A.
Figure 3
 
Identification and relative quantification of coronin-1A using iTRAQ. (A) The histograms indicate the Log2 fold changes in coronin-1A between the allograft transplantation group, autologous keratoplasty group (traumatic control group), and blank control group. Higher bars mean larger fold changes. (B) Sequences containing the DAGPLLISLK peptide, which forms coronin-1A. The histograms indicate the normalized Log2 intensity of each labeling channel, which reflects the peptide abundance in each sample. (C) Another identified peptide sequence comprising KSDLFQEDLYPPTAGPDPALTAEEWLSGR, leading to the identification of coronin-1A.
Confirmation of Coronin-1A
For confirmation purposes, the expression of coronin-1A protein identified in the quantitative study was validated by Western blotting. The western blotting data for the transplanted right eyes were consistent with the findings of the MS analysis, validating the differential expression of coronin-1A in tear samples from PKP model rats. Conversely, the concentration of coronin-1A in the nontransplanted left eyes remained at a low level in all groups. Representative images and relative gray ratios are shown in Figure 4
Figure 4
 
Validation of coronin-1A by Western blotting. (A) Representative images of coronin-1A for the eyes that underwent surgery (β-actin was evaluated as a control for protein loading). (B) Representative images of coronin-1A for the eyes that did not undergo surgery (β-actin was evaluated as a control for protein loading). (C) Bands of the relative gray ratios for coronin-1A.
Figure 4
 
Validation of coronin-1A by Western blotting. (A) Representative images of coronin-1A for the eyes that underwent surgery (β-actin was evaluated as a control for protein loading). (B) Representative images of coronin-1A for the eyes that did not undergo surgery (β-actin was evaluated as a control for protein loading). (C) Bands of the relative gray ratios for coronin-1A.
Discussion
To best of our knowledge, this study is the first to report on the use of quantitative proteomic technology to identify differentially expressed tear proteins and potential biomarkers for acute corneal allograft rejection in a rat model. Besides the blank control group, an autograft group (traumatic control group) was examined to exclude any interference of surgical trauma during the data analysis. We demonstrated that iTRAQ proteomics is a powerful method for understanding the molecular mechanisms underlying acute corneal allograft rejection from an overall and dynamic viewpoint. In our study, a set of 269 proteins was quantified, including 118 significant proteins differentially expressed by more than 2.0- or less than 0.5-fold with greater than 95.0% probability. Finally, we narrowed down our potential protein to coronin-1A, a novel candidate biomarker that might play a vital role in the early stage of acute corneal rejection, and applied Western blotting to verify coronin-1A expression in tear samples from both eyes. 
To date, only one previous study has applied proteomic technology to observe aqueous humor proteins after corneal transplantation.20 In that study, 31 protein spots were upregulated in the rejection group compared with the control groups. The spots were derived from albumin, α1-antitrypsin, apolipoprotein J, cytokeratin type II, serine proteinase inhibitor, and transthyretin. These differentially expressed proteins suggest that breakdown of the aqueous–blood barrier, enzymatic degradation, and liberation of locally synthesized proteins might be three potential mechanisms associated with proteomic changes. Given that anterior chamber–associated immune deviation is one of the mechanisms underlying immune privilege of the cornea,21 observations of aqueous humor proteins are promising for understanding the mechanisms of corneal rejection. Tears, which are another type of fluid, also interact with corneal metabolism,22 and collection of tears is more convenient than collection of aqueous humor. If tear proteomic studies can provide credible information, they would be an alternative choice for further studies on acute corneal rejection. 
In our research, we mixed 14 individual tear specimens collected from one group at the same time point for subsequent iTRAQ labeling. It has been reported that mixing of individual samples is a good way to remove individual differences, and that when more than seven individual samples are pooled, the variations are not significant to the whole pool.23 On the other hand, owing to the limited volume of tears collected from individual rats and the tiny possible differences in expressed proteins, it is an acceptable way to improve the detection rate of low-abundance proteins. 
Removal of High-Abundance Proteins
A complication when conducting proteomic analyses, regardless of the experimental approach, is the very wide concentration range in biological samples.24 Disease biomarkers usually appear at low concentrations, and may be masked by high-abundance proteins. Therefore, it is practical to remove nonspecific high-abundance proteins, thus enhancing disease-related low-abundance proteins.25 Many proteomic studies have applied high-abundance protein removal techniques to body fluids such as serum,26 cerebrospinal fluid, synovial fluid,27 and saliva.28 Several high-abundance proteins (e.g., lysozyme, lipocalin, lactoferrin, transferrin, and immunoglobulin) that have been reported in the tear fluid of animals29 might be altered in certain ocular surface disorders, such as dry eye, keratoconus, and external ocular surgery.68,10,30 However, alterations to high-abundance proteins actually may be detected in nearly all external ocular disorders by efficient proteomic techniques, and thus cannot be considered as specific biomarkers for new diagnostic procedures and treatment options for a certain disease. Taking all of these factors into consideration, we depleted a set of high-abundance proteins, hoping to discover some specific new potential proteins vital for the process of acute corneal rejection. 
Gene Ontology Annotation and Pathway Analysis
It is interesting that the top enrichment among the GO annotations was neurological disease. It has been reported that some neurotransmitters interact directly with T-cell–expressed receptors, leading to the activation or suppression of various T-cell functions, including cytokine secretion, proliferation, adhesion, and migration.31 Neurological diseases also may lead to related immune responses.32 The results of our study suggest that proteins involved in neurological regulation also may participate in the immunological process of acute corneal rejection. Exploring the cross-talk between the nervous and immune systems might hold great promise for further understanding of the mechanisms underlying acute corneal rejection. 
In the pathway analyses, many identified proteins were involved in LXR/RXR activation. The LXR/RXR can regulate the transcription of several genes involved in cholesterol metabolism, lipid metabolism, and glucose metabolism.33,34 High enrichment of LXR/RXR activation in the tears of PKP model rats may indicate that allograft rejection is related to these metabolic processes. Previous studies also have revealed that LXR/RXR are negative regulators of macrophage inflammatory gene expressions, such as inducible nitric oxide synthase, IL-6, IL-1, and matrix metalloprotein-9, which are closely related to corneal allograft rejection.35,36 
Potential Protein Candidate for Acute Corneal Rejection
To exclude the possibility that the potential protein biomarkers for acute corneal allograft rejection are also involved in other ocular surface conditions, we have searched the current literature to confirm that our proposed protein has not been studied in other ocular surface disorders. Coronin-1A plays a role in cell movement, which was one of the top four enrichment functions based on the GO annotations. It was reasonable to choose a candidate with high enrichment for the bioinformatics analysis. Furthermore, coronin-1A was selected as a totally new potential protein for acute corneal allograft rejection because it was upregulated in all samples from the allograft group compared with those from the autologous group. Furthermore, in the allograft group, the expression of coronin-1A was significantly increased on day 7, when rejection began to occur, and maintained at a high level on day 14. 
Among the various coronin family members, coronin-1A is preferentially expressed in cells of hematopoietic origin, where it is coexpressed with other more widely expressed coronin family members, including coronin-2, -3, and -7.37 Coronin-1A colocalizes with F-actins surrounding phagocytic vesicles in neutrophils and macrophages and F-actin–rich membranes in activated T cells.38,39 The T cells are one of the effector cell types involved in the process of solid organ transplantation rejection. The interactions of T cells and chemokines are vital for T-cell migration to transplanted organs. Coronin-1A−/− T cells show defects in chemokine-mediated migration and lymphocyte homeostasis.40 Based on the Ingenuity Pathways Analysis literature database, coronin-1A has direct and indirect interactions with many chemokines (Fig. 5). Coronin-1A also can promote a cytoskeletal-based feedback loop that facilitates Rac1 translocation and activation,41 and Rac1 is involved in the polarity and motility of T cells (i.e., their ability to move, scan, and form functional immunological synapses with antigen-presenting cells).42 Although a previous proteomic study demonstrated that coronin-1A is significantly upregulated in acute allograft rejection,43 we are the first to propose the hypothesis that coronin-1A might be both an indispensable factor and a potential biomarker for acute corneal allograft rejection. 
Figure 5
 
Direct and indirect interactions of coronin-1A with other chemokines (solid line: direct interaction; broken line: indirect interaction).
Figure 5
 
Direct and indirect interactions of coronin-1A with other chemokines (solid line: direct interaction; broken line: indirect interaction).
Coronin-1A is required for activation of the Ca2+-dependent phosphatase calcineurin. In mycobacterial infections, coronin-1A can block the lysosomal delivery of mycobacteria by activating calcineurin. Conversely, in the absence of coronin-1A, calcineurin activity does not occur, resulting in lysosomal delivery and killing of the mycobacteria. Strikingly, genetic depletion of coronin-1A can be phenocopied by adding the calcineurin inhibitors cyclosporine A and FK506.44,45 With their high degree of specificity for T-cell lymphocytes and ability to act as calcineurin inhibitors to prevent T-cell–mediated immune responses, these two drugs are considered to be first-line agents for antirejection after keratoplasty.46 These findings further confirm our hypothesis that coronin-1A may play a significant role in the process of acute corneal rejection. 
Conclusions
In summary, the present study is the first to describe proteome profiling of tears at different time points in a rat model with acute rejection after PKP. We have demonstrated that iTRAQ is a valuable and powerful tool for identifying potential biomarkers and permitting a better overall and dynamic understanding of the rejection process. Further studies are necessary to validate the specific function of coronin-1A in acute corneal allograft rejection. 
Acknowledgments
Supported by Shanghai Committee of Science and Technology Foundation (10411962000). 
Disclosure: F. Huang, None; J. Xu, None; H. Jin, None; J. Tan, None; C. Zhang, None 
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Figure 1
 
Experimental design. (A) Entire experimental design. (B) iTRAQ design.
Figure 1
 
Experimental design. (A) Entire experimental design. (B) iTRAQ design.
Figure 2
 
Hierarchical cluster analysis of proteins identified in this study. Green signifies downregulated proteins and red signifies upregulated proteins. The clustering shows that in the allograft group, the expression patterns at day 7 are more similar to those at day 2 than to those at day 14; whereas in the autograft group (traumatic control group), the expression patterns at day 7 are more similar to those at day 14 than to those at day 2. Overall, the expression patterns between the allograft group and the autograft group are quite different. Coronin-1A is featured to show its expression changes at different time points in both the allograft and autograft groups.
Figure 2
 
Hierarchical cluster analysis of proteins identified in this study. Green signifies downregulated proteins and red signifies upregulated proteins. The clustering shows that in the allograft group, the expression patterns at day 7 are more similar to those at day 2 than to those at day 14; whereas in the autograft group (traumatic control group), the expression patterns at day 7 are more similar to those at day 14 than to those at day 2. Overall, the expression patterns between the allograft group and the autograft group are quite different. Coronin-1A is featured to show its expression changes at different time points in both the allograft and autograft groups.
Figure 3
 
Identification and relative quantification of coronin-1A using iTRAQ. (A) The histograms indicate the Log2 fold changes in coronin-1A between the allograft transplantation group, autologous keratoplasty group (traumatic control group), and blank control group. Higher bars mean larger fold changes. (B) Sequences containing the DAGPLLISLK peptide, which forms coronin-1A. The histograms indicate the normalized Log2 intensity of each labeling channel, which reflects the peptide abundance in each sample. (C) Another identified peptide sequence comprising KSDLFQEDLYPPTAGPDPALTAEEWLSGR, leading to the identification of coronin-1A.
Figure 3
 
Identification and relative quantification of coronin-1A using iTRAQ. (A) The histograms indicate the Log2 fold changes in coronin-1A between the allograft transplantation group, autologous keratoplasty group (traumatic control group), and blank control group. Higher bars mean larger fold changes. (B) Sequences containing the DAGPLLISLK peptide, which forms coronin-1A. The histograms indicate the normalized Log2 intensity of each labeling channel, which reflects the peptide abundance in each sample. (C) Another identified peptide sequence comprising KSDLFQEDLYPPTAGPDPALTAEEWLSGR, leading to the identification of coronin-1A.
Figure 4
 
Validation of coronin-1A by Western blotting. (A) Representative images of coronin-1A for the eyes that underwent surgery (β-actin was evaluated as a control for protein loading). (B) Representative images of coronin-1A for the eyes that did not undergo surgery (β-actin was evaluated as a control for protein loading). (C) Bands of the relative gray ratios for coronin-1A.
Figure 4
 
Validation of coronin-1A by Western blotting. (A) Representative images of coronin-1A for the eyes that underwent surgery (β-actin was evaluated as a control for protein loading). (B) Representative images of coronin-1A for the eyes that did not undergo surgery (β-actin was evaluated as a control for protein loading). (C) Bands of the relative gray ratios for coronin-1A.
Figure 5
 
Direct and indirect interactions of coronin-1A with other chemokines (solid line: direct interaction; broken line: indirect interaction).
Figure 5
 
Direct and indirect interactions of coronin-1A with other chemokines (solid line: direct interaction; broken line: indirect interaction).
Table
 
Clinical Evaluation of Rat PKP Model (Mean ± SD)
Table
 
Clinical Evaluation of Rat PKP Model (Mean ± SD)
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