Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Proteome Res. Author manuscript; available in PMC 2010 December 1.
Published in final edited form as:
PMCID: PMC2798584

Angiotensin AT1 receptor antagonism ameliorates murine retinal proteome changes induced by diabetes


Diabetic retinopathy is the most common microvascular complication caused by diabetes mellitus and is a leading cause of vision loss among working-age adults in developed countries. Understanding the effects of diabetes on the retinal proteome may provide insights into factors and mechanisms responsible for this disease. We have performed a comprehensive proteomic analysis and comparison of retina from C57BL/6 mice with 2 months of streptozotocin-induced diabetes and age-matched nondiabetic control mice. To explore the role of the angiotensin AT1 receptor in the retinal proteome in diabetes, a subgroup of mice were treated with the AT1 antagonist candesartan. We identified 1,792 proteins from retinal lysates, of which 65 proteins were differentially changed more than 2 fold in diabetic mice compared with nondiabetic mice. The majority (72%) of these protein changes were normalized by candesartan treatment. Most of the significantly changed proteins were associated with metabolism, oxidative phosphorylation, and apoptotic pathways. An analysis of the proteomics data revealed metabolic and apoptotic abnormalities in the retina from diabetic mice that were ameliorated with candesartan treatment. These results provide insight into the effects of diabetes on the retina and the role of the AT1 receptor in modulating this response.

Keywords: angiotensin AT1 receptor, diabetes mellitus, diabetic retinopathy, proteome, retina


Diabetic retinopathy (DR) is a sight-threatening disease that develops to some extent in nearly all people with diabetes mellitus. Although DR is primarily characterized as a vascular disease, there is growing evidence that the glial and neural components of retina are also involved. Early vascular changes in DR include increased retinal vascular permeability (RVP), the appearance of microaneurysms and hemorrhages, and changes in vessel diameter and hemodynamics 1. In addition, diabetes can lead to acellular capillaries and pericyte loss, which have been implicated in causing areas of retinal ischemia and the resultant pathological new vessel growth. Diabetes also induces abnormal electroretinogram responses, suggesting early and regional changes in neuroretinal responses or conduction 2. Although the incidence and progression of DR are reduced with intensive glycemic control, additional therapeutic strategies to prevent this disease are needed.

Experimental and clinical studies suggest that inhibition of the renin-angiotensin system (RAS) may reduce DR 34. In animal studies, blockade of RAS was effective in reducing RVP, vascular endothelial growth factor expression, electroretinogram, and hemodynamic abnormalities induced by diabetes 57. Evidence supporting the importance of RAS inhibition in slowing the progression of retinopathy in humans has been obtained from several trials in both type 1 and type 2 diabetes. The EUCLID (EURODIAB Controlled Trial of Lisinopril in Insulin-Dependent Diabetes Mellitus) Study indicated a beneficial effect of angiotensin-converting enzyme (ACE) inhibitor treatment on DR in people with type 1 diabetes. In addition, the UKPDS (UK Prospective Diabetes Study) demonstrated a reduction in the need for laser photocoagulation in type 2 patients who received an ACE inhibitor 8. The Diabetic Retinopathy Candesartan Trials (DIRECT) Programme reported that candesartan reduced the incidence of retinopathy in type 1 diabetes by 18% for a 2-step change (EDTRS scale, primary outcome), 35% for a 3-step change, and had no effect on progression of retinopathy in type 1 diabetes 9. In type 2 diabetes, candesartan treatment resulted in 34% regression of retinopathy10. Importantly, an overall significant change towards less-severe retinopathy was noted in both type 1 and 2 diabetes. A recent study showed that early blockade of RAS in patients with type 1 diabetes did not slow nephropathy progression but did slow the progression of retinopathy11.

Streptozotocin (STZ) -induced diabetes in rats and mice has been used extensively for analysis of DR. In mouse retina, diabetes increases RVP, apoptosis, and neuronal cell death 1213. Gene expression profiling studies on retina from rats and mice subjected to 1 and 3 months of diabetes revealed upregulation of genes associated with oxidative phosphorylation and protein turnover 1415. A recent two-dimensional gel-based proteomic analysis of rat retina identified 168 proteins. The study showed that beta catenin, phosducin, and aldehyde reductase were increased in retina from rats with STZ-induced diabetes; whereas succinyl CoA ligase and dihydropyrimidase-related protein were decreased by diabetes 16.

In our study, we characterized the mouse retinal proteome and examined the effects of diabetes in the absence or presence of treatment with the angiotensin AT1 receptor blocker, candesartan, on protein expression profiles in the retina.

Materials and Methods

Experimental Animals

Animals were group-housed with full access to standard laboratory chow and water at a room temperature of 23 ± 1 °C in a 12-h light/12-h dark cycle. To induce diabetes, 7–8 weeks old male C57BL/6 mice were injected intraperitoneally with 45 mg/kg STZ (Sigma–Aldrich, St. Louis, MO) in 10 mM citrate buffer vehicle over a 5 day interval. Nondiabetic mice (NDM, n = 11) were injected with the citrate buffer only. Animals with blood glucose levels greater than 250 mg/dl 72 h later were considered diabetic mice (DM, n = 21). Diabetic mice assigned to the treatment group (DMC, n = 11) received candesartan-cilexetil (Astrazeneca) ab libitum in drinking water at the dose of 10 mg/kg/day beginning 3 days after the completion of the STZ injection protocol and confirmation of diabetes onset. Blood pressure was measured by a noninvasive tail cuff sensor and monitoring system (Visitech Systems, Inc., Apex, NC). Blood glucose levels were measured using an Advantage II Accu-Check monitor. Two months after injection, mice were deeply anaesthetized by an intraperitoneal injection of sodium pentobarbital (Nembutal), and subsequently euthanized by cervical dislocation. Eyes were immediately enucleated, the retina isolated from the retinal pigment epithelium and then immediately frozen in liquid nitrogen.

Identification of Retinal Proteins by LC-MS/MS

Both retina from each mouse were combined and retinal protein extracted by sonication in ice-cold lysis buffer containing 50 mM Hepes (pH 7.4), 150 mM NaCl, 4 mM EDTA, 10 mM Na4P2O7, 100 mM NaF, 2 mM Na3VO4, 1 mM PMSF, 0.1 mg/ml aprotinin, 10% glycerol and 1% Triton X-100. Lysates were centrifuged at 20,000 g for 30 min. The protein concentration in the supernatant was determined by Bio-Rad Protein Assay (Bio-Rad, Hercules, CA). Retinal lysates (200 µg) from 5 mice in each group were separated by 10 % SDS-PAGE. The SDS-PAGE gel was stained with Coomassie Brilliant Blue G-250 stain (Bio-Rad, Hercules, CA) and destained in 10% glacial acetic acid and 40% methanol. The entire lane for each sample was divided into 40 slices. Gel slices were individually digested with trypsin (Promega, Madison, WI). Gel tryptic digests were analyzed by tandem mass spectrometry using a LTQ linear ion trap mass spectrometer (Thermo Scientific, San Jose, CA).

Assignment of MS/MS data was performed using X!Tandem (version 2006.09.15, The Global Proteome Machine Organization) search against the International Protein Index (IPI) mouse sequence database (IPI_MOUSE_v3.39, 52777 sequences, European Bioinformatics Institute) and a randomized version of the same IPI database generated by a Perl script, (Matrix Science, London, UK). The default X!Tandem search parameters were used, except for the following: a maximum valid expectation value of 0.1; potential residue mass modification of +16.0 Da for oxidized methionine and +71.0 Da for acrylamide alkylated cysteine; spectrum parameters including a fragment monoisotopic mass error of ± 0.4 Da and a precursor mono-isotopic mass error of ± 0.5 Da.

Compilation of Search Results

Search results were compiled into a MySQL database and analyzed using MS Results Manager, a proteomics computational analysis software based on the PHP-MySQL-Apache platform 17. Data processing occurred in five steps: i) File parsing: X!Tandem search results were parsed into the MySQL database. ii) Summarizing: The search results from each sample, generated from 40 gel slices, were combined based on IPI identifier (ID). iii) Protein-level filtering: The redundant proteins, contaminates, and proteins that did not have 2 unique peptides identified from a single slice or adjacent slices were filtered. iv) Reporting: Proteins that were identified in at least 2 samples were complied into one reporting table. If multiple IDs were assigned for the same peptide match, a uniform ID was selected for the comparison of proteins identified between samples. The false discovery rate (FDR) of protein identification was calculated by dividing the number of random sequences by the sum of “random” and “real” sequences and multiplying by 100. Spectral count (the total number of observations of spectral-peptides matches) for each protein was calculated by the summation of peptides matched.

Bioinformatics Analysis

Gene Ontology (GO) annotations were extracted from Gene Ontology Annotation Database (GOA Mouse 49.0) 18 and generic GO slim provided by European Bioinformatics Institute and the Gene Ontology 19.


Students’ t-tests and one-way ANOVA were performed by in-house PHP script based on PHP statistics extension, or GraphPAD Prism (GraphPAD Software, San Diego, CA). The results represent the total spectral count (mean ± S.E.M). Values of P < 0.05 were considered significantly different.


Blood Glucose and Blood Pressure

DM had significantly higher blood glucose (530.5 ± 26.0 vs. 142.9 ± 8.9 mg/dl, P < 0.001, Fig.1a) and less body weight gain (24.3 ± 0.7 vs. 33.4 ±1.3 g, P < 0.001, Fig.1b) compared to NDM. Blood pressure was similar in DM and NDM mice (Fig. 1c and d). Candesartan treatment had no effect on blood glucose (476.7 ± 26.1 vs. 530.5 ± 26.0 mg/dl, Fig.1a) or body weight (23.3 ± 0.7 vs. 24.3 ± 0.7 g, Fig.1b) but did significantly reduce both systolic blood pressure (93.4 ± 1.0 vs. 69.7 ± 8.2 mmHg, P < 0.01, Fig.1c) and diastolic blood pressure (59.0 ± 2.6 vs. 42.0 ± 3.6 mmHg, P < 0.01, Fig.1d) compared with untreated DM group.

Figure 1
Characteristics of diabetic mice (DM) and nondiabetic mice (NDM)

Identification of the Proteins in Mouse Retina

The retina from NDM, DM and DMC (n = 5) were subjected to proteomics analysis. Two hundred µg of retinal tissue lysate were fractionated by 10% SDS-PAGE. Each lane was excised into 40 slices followed by tryptic digestion and the digests were analyzed by LC-MS/MS. The resultant data was analyzed by X!Tandem (Supporting Information Figure shows two representative MS/MS spectra) and MS Result Manager. The analysis of 15 samples and 600 gel slices led to the identification of a total of 1,792 proteins (Supporting Information Table) with a FDR (for protein identification) of 0.28%. In order to get an overview of the ontology content, Gene Ontology (GO) slim terms were used to categorize the retinal proteome. The GOA Mouse database was also categorized by GO slim terms for comparison (Table 1). There are 52,777 proteins in IPI_MOUSE_v3.39 database and 33,691 proteins (64%) that are annotated proteins in GOA Mouse 49.0 database. If multiple IDs were assigned for the same peptide match, the ID that had more GO term annotations was selected. This led to a high percentage (95.76%) of annotated proteins in the retinal proteome. Compared to the proteins in the GOA mouse database, the analyses of the biological process terms distribution in the observed retinal proteome showed greater than 2 fold distribution of proteins with GO term categories related to amino acid and derivative metabolism (2.64 fold), biosynthesis (2.14 fold), catabolism (2.65 fold), and electron transport (5.45 fold). These analyses of the cellular component terms distribution showed there are more cytoplasm proteins (2.49 fold) and less cell surface proteins (0.33 fold). The analyses of the molecular function terms distribution showed there are more proteins related to isomerase activity (3.41 fold), ligase activity (2.43 fold), lyase activity (2.85 fold), oxidoreductase activity (2.15 fold), translation regulator activity (2.77 fold), protein transporter activity (2.69 fold) and less proteins related to signal transducer activity (0.24 fold), receptor activity (0.11 fold), transcriptional regulator activity (0.40 fold), and channel or pore class transporter activity (0.32 fold) (Table 1).

Table 1
Frequency of gene ontology terms in murine retinal proteome annotation

Previous reports indicate that spectral counting can be used as a semi-quantitative measurement of protein abundance 2021. The most abundant proteins in the retina include the glycolytic enzymes: alpha-enolase, pyruvate kinase isozymes M1/M2, glyceraldehyde-3-phosphate-dehydrogenase isoform 1, and fructose-bisphosphate aldolase A; S-arrestin, one of the major proteins of the rod photoreceptor; tubulin alpha-1C, a major constituent of microtubules, and beta-globin (Supporting Information Table).

Differentially Expressed Proteins in DM Retina

The protein compositions of retina from the NDM and DM group were compared by spectral counting for each protein from each sample. Each protein was usually identified in multiple slices, which were visualized by an output of spectral counts per slice as grayscale digital images (Fig. 2). We identified 170 proteins that were differentially expressed in DM retina with P < 0.05. The proteins changed with P < 0.05 may be caused by type 1 errors in multiple comparisons. The coefficient of variation generated from spectral counting is high when there are a few peptides identified in each protein 17. Therefore, we introduced another FDR for significance to evaluate the peptide number cutoff. The FDR for significance was calculated by dividing the significantly changed protein number of a random group by the experiment group and then multiplying by 100. We randomly divided the samples into 3 groups and performed statistics. The FDR for significance dropped from 24.7% (all matched proteins) to 17.32% and 15.5% when the peptide cutoff was set to 3 and 6, respectively (Fig. 3a). No further decreases were found with increased peptide cutoffs. Based on these results, we selected a peptide cutoff of at least 3 peptides and no more than 6 peptides in this study. The selection of fold change also affected FDR for significance. The FDR for significance dropped from 16.16% to 14.24% and 9.23% when the fold change was set to ≥ 1.5 and ≥ 2.0, respectively (Fig. 3b). Therefore, the criteria for differentially expressed proteins used for further analysis were defined for this study as: P < 0.05, fold change ≥ 2.0, and the average total peptides >5 for either group. We identified 65 proteins (3.62% of total proteins) that were differentially expressed in the DM retina compared with the NDM group (Table 2), comprised of 55 proteins that were increased and 10 proteins that were decreased. After gene-annotation enrichment analysis and functional annotation clustering analysis by DAVID, 23 proteins were enzymes, 40 proteins were related to metabolic processes, 19 proteins were related to protein metabolic processes, and 5 proteins were related to lipid metabolic processes. Sixteen proteins related to protein metabolism that were increased in the DM retina are: prefoldin subunit 2 (Pfdn2), coatomer subunit zeta-1 (Copz1), eukaryotic translation elongation factor 1 delta (Eef1d), eukaryotic translation initiation factor 2 subunit 1(Eif2s1), sarcolemmal membrane-associated protein (Slmap), glycyl-tRNA synthetase (Gars), AP2-associated protein kinase 1 (Aak1), heat shock 70 kDa protein 12A (Hspa12a), 60S ribosomal protein L23a (Rpl23), 60S ribosomal protein L21 (Rpl21), 9S ribosomal protein L12 (Mrpl12), 26S protease regulatory subunit S10B (Psmc6), dual specificity protein phosphatase 3 (Dusp3 ), FK506-binding protein 4 (Fkbp4), MAGUK p55 subfamily member 2 (Mpp2), and AP-3 complex subunit beta-2 (Ap3b2). Three proteins related to protein metabolic processes that were decreased were: ubiquitin-conjugating enzyme E2N (Ube2n), protein phosphatase methylesterase 1 (Ppme1), and FK506-binding protein 2 (Fkbp2). Increased levels of proteins in the DM retina related to lipid metabolic processes included: fatty acid synthase (Fasn), long-chain-fatty-acid--CoA ligase 3 (Acsl3), estradiol 17-beta-dehydrogenase 8 (H2-Ke6), retinol dehydrogenase 12 (Rdh12), NADH-cytochrome b5 reductase 3 (Cyb5r3). Three proteins associated with apoptosis were increased in retina from diabetic mice, including: apoptosis inhibitor 5 (Api5), dynamin-like 120 kDa protein (Opa1), and NADH-ubiquinone oxidoreductase 75 kDa subunit (Ndufs1) 22. Conversely, astrocytic phosphoprotein PEA-15 (Pea15a) was decreased.

Figure 2
Extracted spectral counting of Hspa12a and Rdh12 from retina of diabetic mice (DM), nondiabetic mice (NDM), and diabetic mice treated with candesartan (DMC) displayed as grayscale digital image
Figure 3
The selection of peptide count (a) and fold change (b) affected the false discovery rate
Table 2
Proteomic differences in retina from diabetic mice (DM) compared with nondiabetic mice (NDM)

Effect of Candesartan on Expressed Proteins in DM Retina

In order to examine the effect of candesartan on the proteome change, proteome analysis was performed on the candesartan-treated DM (DMC) retina. Eightynine proteins were differentially expressed in retina from DMC compared with the untreated DM group. However, only 31 proteins were differentially expressed between NDM and DMC retina. Of the 65 proteins that were differentially expressed in DM retina compared with the NDM group, 24 proteins were reversed (DM vs. DMC, P < 0.05; Fig. 4a) and 23 proteins were reversed by at least 50% (P > 0.05) with the treatment of candesartan. Six proteins were unchanged by the treatment of candesartan (Fig. 4b). This result suggests that candesartan ameliorated abnormalities in 72% of the proteins altered in diabetic mice. Proteins related to lipid metabolism (Fasn, Acsl3, H2-Ke6, Rdh12, Cyb5r3) and proteins related to apoptosis regulation (Api5, Opa1, Pea15a) were normalized after candesartan treatment.

Figure 4
Effect of candesartan on proteins in diabetic mouse retina


In this report we have 1) generated a murine retinal proteomic inventory, 2) identified a group of proteins that are altered in mice subjected to 2 months of diabetes, and 3) evaluated the effect of angiotensin AT1 receptor antagonism on the diabetic retinal proteome. A recent study using the 2-DE technique detected 168 proteins in the normal rat retina and identified in gels displaying retinal proteins from diabetic rats 24 unique proteins, 37 absent proteins, 8 spots with increased expression, and 27 with decreased expression 16. In this study, we performed a comprehensive proteomics analysis of the mouse retina under diabetic conditions and with candesartan treatment using a 1D-PAGE-LC/MS/MS technique. Using this approach, we identified 1,792 unique proteins in the murine retinal proteome with an FDR of 0.28%. Comparing this result to the SAGE (serial analysis of gene expression) study in which 3,834 unique known genes have been found to be expressed in the retina 23, the proteome list provided in this study is an informative resource, particularly in relation to retinal protein changes in diabetes. The GO term analysis provided a broad view of the retinal proteome, with several notable traits in the proteome list compared with all annotated mouse genes. In the retinal proteome, we observed more cytoplasm proteins and more proteins related to metabolism, electron transport, translational regulator activity, and protein transporter activity. We also observed less cell surface proteins and fewer proteins related to signal transducer activity, receptor activity, transcription regulator activity, and channel transporter activity. There are several possible explanations for these results. Firstly, the signal transducer, receptor, transcription regulator, and channel proteins are usually relatively low in abundance and require additional enrichment methods for detection from lysates by LC-MS/MS. Secondly, the retina is the most metabolically active tissue in the body and therefore may be enriched in metabolic proteins. Lastly, the tissue extraction method used may have a higher efficiency of recovering cytosolic proteins compared to membrane associated proteins.

Previous studies reported that a number of genes for inflammatory processes were altered during the first week of STZ-induced diabetes in rat retina 24. After 1 and 3 months of STZ injection, most of the upregulated genes could be classified into two functional categories: oxidative phosphorylation and protein turnover 15. However, large-scale analyses of mRNA expression and protein abundance data showed that the correlation between mRNA and protein levels was insufficient to predict protein expression levels from quantitative mRNA data because of differences in translational and post-translational processing 2526. Quantitative proteomic approaches based on the number of spectral counts have been widely applied on large scale and complex samples 2021. Quantitative analysis based on spectral count was also successfully applied on 1D-PAGE LC/MS/MS-based proteomics in our previous human vitreous proteomics studies 2728. In this study, the changes in retinal protein levels were examined in NDM and DM mice 2 months after the injection of STZ. We found that 65 proteins were differentially changed more than 2-fold in DM retina compared with NDM. After GO annotation analysis, the categories of these proteins were: protein metabolism, lipid metabolism, apoptosis, and vision.

Metabolic abnormalities are integral in the development of retinopathy in diabetic mice 29. In our study, 40% of the differentially expressed proteins in the retina from diabetic mice were related to metabolic processes, of which 19 proteins were related to protein metabolic processes. This result is consistent with previous studies that reported genes involved in energy production and protein synthesis were highly expressed in the retina 30 and genes for protein turnover were upregulated in the retina after 2 months of STZ-induced diabetes 15. In addition, several proteins related to lipid metabolism were also increased in the DM retina including: fatty acid synthase (Fasn), long-chain-fatty-acid--CoA ligase 3 (Acsl3), estradiol 17-beta-dehydrogenase 8 (H2-Ke6), retinol dehydrogenase 12 (Rdh12), and NADH-cytochrome b5 reductase 3 (Cyb5r3).

Hyperglycemia causes increased production of mitochondrial reactive oxygen species (ROS), which has been implicated in contributing to the clinical complications associated with diabetes and obesity 31. We found that two oxidative phosphorylation-related proteins (NADH-ubiquinone oxidoreductase 75 kDa subunit (Ndufs1) and NADH dehydrogenase ubiquinone iron-sulfur protein 2 (Ndufs2)) were increased in the retina from diabetic mice.

Apoptosis of retinal neural cells and vascular pericytes and the proliferation of endothelial cells have been well established as early changes in DR 3234. The mechanisms by which diabetes influences apoptosis in the retina are not fully understood. In our study, three proteins related to apoptosis regulation (apoptosis inhibitor 5 (Api5), dynamin-like 120 kDa protein (Opa1), and NADH-ubiquinone oxidoreductase 75 kDa subunit (Ndufs1) 22) were increased. Another apoptosis-related protein, astrocytic phosphoprotein PEA-15 (Pea15a), was decreased in DM retina. Api5 was initially isolated as a gene whose expression promoted cell survival following serum deprivation 35. Api5 was identified as a suppressor of E2F-dependent apoptosis in vivo 36. Opa1 is highly expressed in rat and human retina 3738, and mutation in Opa1 is associated with autosomal dominant optic atrophy 39. PEA-15 is a small protein (15 kDa) that was first identified as an abundant phosphoprotein in brain astrocytes and is an endogenous protein that inhibits FAS and TNFR1-mediated apoptosis 40. PEA-15 may play a major role in signal integration and has been shown to modulate signaling pathways that control apoptosis and cell proliferation. In particular, PEA-15 protects astrocytes from TNFα-triggered apoptosis and regulates the actions of the ERK MAP kinase cascade by binding to ERK and altering its subcellular localization 41.

Experimental and clinical studies have suggested that the RAS plays a role in the pathogenesis of DR. ACE inhibition and angiotensin receptor blockade may reduce the progression of DR. The beneficial effects of inhibiting angiotensin AT1 receptor signaling may include: inhibiting leukocyte adhesion to the retinal vasculature 42, reducing overexpression of vascular endothelial growth factor and hyperpermeability 43, normalizing retinal blood flow 44, and ameliorating neuronal dysfunction 45. The results from our proteomics study show that 72% of the differentially abundant proteins isolated from STZ-induced diabetic mice retina could be prevented by candesartan treatment. In particular, we found that the proteins related to lipid metabolism (Fasn, Acsl3, H2-Ke6, Rdh12, Cyb5r3) and apoptosis regulation (Api5, Opa1, Pea15a) were normalized with candesartan treatment.

In summary, we have provided a proteomic inventory of the mouse retina with 65 proteins involved in metabolic processes and apoptosis regulation that are altered in DM mice retina. The majority of the upregulated proteins are classified under four categories: metabolism, oxidative phosphorylation, and apoptosis. Treatment of mice with the angiotensin AT1 receptor antagonist, candesartan, prevented the majority of protein changes induced by diabetes. The proteomics data identified proteins involved in metabolism and apoptosis that are abnormally abundant in the retina from diabetic mice, and they may contribute to the development of DR. These results provide insight into new possible preventative and therapeutic interventions of DR.

Supplementary Material


Click here to view.(1.3M, excel)



This work was supported in part by the US National Institutes of Health (grants EY019029, DK 36836), and the Massachusetts Lions Eye Research Fund. B-B Gao is a recipient of a Mary K. Iacocca Fellowship and JA Phipps is supported by an NHMRC (Australia) CJ Martin Fellowship. We thank Jane Chen for assistance with proteomic analyses, Drs. Stephen Kolwicz Jr and Jia Liu for their critical comments regarding this manuscript, and Astrazeneca for providing candesartan-cilexetil.


The authors have declared no conflict of interest.

Supporting Information Available: Supporting Information Figure: Representative peptide coverage and MS/MS spectra of Hspa12a and Rdh12.

Supporting Information Table: Retinal proteome in nondiabetic and diabetic mice. This Excel table contains a non-redundant list of IPI, protein name, gene symbol, molecular weight, sequence coverage, total peptides, unique peptides, spectral count of diabetic mice, nondiabetic mice, and diabetic mice treated with candesartan (mean ± S.E.M. n = 5), and redundant protein name for the 1,792 proteins identified in this study. This information is available free of charge via the Internet at


1. Antonetti DA, Barber AJ, Bronson SK, Freeman WM, Gardner TW, Jefferson LS, Kester M, Kimball SR, Krady JK, LaNoue KF, Norbury CC, Quinn PG, Sandirasegarane L, Simpson IA. Diabetic retinopathy: seeing beyond glucose-induced microvascular disease. Diabetes. 2006;55(9):2401–2411. [PubMed]
2. Fortune B, Schneck ME, Adams AJ. Multifocal electroretinogram delays reveal local retinal dysfunction in early diabetic retinopathy. Invest Ophthalmol Vis Sci. 1999;40(11):2638–2651. [PubMed]
3. Clermont A, Bursell SE, Feener EP. Role of the angiotensin II type 1 receptor in the pathogenesis of diabetic retinopathy: effects of blood pressure control and beyond. J Hypertens Suppl. 2006;24(1):S73–S80. [PubMed]
4. Sjolie AK. Prospects for angiotensin receptor blockers in diabetic retinopathy. Diabetes Res Clin Pract. 2007;76 Suppl 1:S31–S39. [PubMed]
5. Nagisa Y, Shintani A, Nakagawa S. The angiotensin II receptor antagonist candesartan cilexetil (TCV-116) ameliorates retinal disorders in rats. Diabetologia. 2001;44(7):883–888. [PubMed]
6. Horio N, Horiguchi M. Retinal blood flow analysis using intraoperative video fluorescein angiography combined with optical fiber-free intravitreal surgery system. Am J Ophthalmol. 2004;138(6):1082–1083. [PubMed]
7. Phipps JA, Clermont AC, Sinha S, Chilcote TJ, Bursell SE, Feener EP. Plasma kallikrein mediates angiotensin II type 1 receptor-stimulated retinal vascular permeability. Hypertension. 2009;53(2):175–181. [PubMed]
8. Group., U.P.D.S. Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. BMJ. 1998;317(7160):703–713. [PMC free article] [PubMed]
9. Chaturvedi N, Porta M, Klein R, Orchard T, Fuller J, Parving HH, Bilous R, Sjolie AK. Effect of candesartan on prevention (DIRECT-Prevent 1) and progression (DIRECT-Protect 1) of retinopathy in type 1 diabetes: randomised, placebo-controlled trials. Lancet. 2008;372(9647):1394–1402. [PubMed]
10. Sjolie AK, Klein R, Porta M, Orchard T, Fuller J, Parving HH, Bilous R, Chaturvedi N. Effect of candesartan on progression and regression of retinopathy in type 2 diabetes (DIRECT-Protect 2): a randomised placebo-controlled trial. Lancet. 2008;372(9647):1385–1393. [PubMed]
11. Mauer M, Zinman B, Gardiner R, Suissa S, Sinaiko A, Strand T, Drummond K, Donnelly S, Goodyer P, Gubler MC, Klein R. Renal and retinal effects of enalapril and losartan in type 1 diabetes. N Engl J Med. 2009;361(1):40–51. [PMC free article] [PubMed]
12. Mohr S, Xi X, Tang J, Kern TS. Caspase activation in retinas of diabetic and galactosemic mice and diabetic patients. Diabetes. 2002;51(4):1172–1179. [PubMed]
13. Martin PM, Roon P, Van Ells TK, Ganapathy V, Smith SB. Death of retinal neurons in streptozotocin-induced diabetic mice. Invest Ophthalmol Vis Sci. 2004;45(9):3330–3336. [PubMed]
14. Brucklacher RM, Patel KM, VanGuilder HD, Bixler GV, Barber AJ, Antonetti DA, Lin CM, LaNoue KF, Gardner TW, Bronson SK, Freeman WM. Whole genome assessment of the retinal response to diabetes reveals a progressive neurovascular inflammatory response. BMC Med Genomics. 2008;(1):26. [PMC free article] [PubMed]
15. Adachi-Uehara N, Kato M, Nimura Y, Seki N, Ishihara A, Matsumoto E, Iwase K, Ohtsuka S, Kodama H, Mizota A, Yamamoto S, Adachi-Usami E, Takiguchi M. Up-regulation of genes for oxidative phosphorylation and protein turnover in diabetic mouse retina. Exp Eye Res. 2006;83(4):849–857. [PubMed]
16. Quin GG, Len AC, Billson FA, Gillies MC. Proteome map of normal rat retina and comparison with the proteome of diabetic rat retina: new insight in the pathogenesis of diabetic retinopathy. Proteomics. 2007;7(15):2636–2650. [PubMed]
17. Gao BB, Stuart L, Feener EP. Label-free quantitative analysis of 1D-PAGE LC/MS/MS proteome: Application on angiotensin II stimulated smooth muscle cells secretome. Mol Cell Proteomics. 2008;7(12):2399–2409. [PMC free article] [PubMed]
18. Camon E, Barrell D, Lee V, Dimmer E, Apweiler R. The Gene Ontology Annotation (GOA) Database--an integrated resource of GO annotations to the UniProt Knowledgebase. In Silico Biol. 2004;4(1):5–6. [PubMed]
19. Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eilbeck K, Lewis S, Marshall B, Mungall C, Richter J, Rubin GM, Blake JA, Bult C, Dolan M, Drabkin H, Eppig JT, Hill DP, Ni L, Ringwald M, Balakrishnan R, Cherry JM, Christie KR, Costanzo MC, Dwight SS, Engel S, Fisk DG, Hirschman JE, Hong EL, Nash RS, Sethuraman A, Theesfeld CL, Botstein D, Dolinski K, Feierbach B, Berardini T, Mundodi S, Rhee SY, Apweiler R, Barrell D, Camon E, Dimmer E, Lee V, Chisholm R, Gaudet P, Kibbe W, Kishore R, Schwarz EM, Sternberg P, Gwinn M, Hannick L, Wortman J, Berriman M, Wood V, de la Cruz N, Tonellato P, Jaiswal P, Seigfried T, White R. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 2004;32(Database issue):D258–D261. [PMC free article] [PubMed]
20. Blondeau F, Ritter B, Allaire PD, Wasiak S, Girard M, Hussain NK, Angers A, Legendre-Guillemin V, Roy L, Boismenu D, Kearney RE, Bell AW, Bergeron JJ, McPherson PS. Tandem MS analysis of brain clathrin-coated vesicles reveals their critical involvement in synaptic vesicle recycling. Proceedings of the National Academy of Sciences of the United States of America. 2004;101(11):3833–3838. [PubMed]
21. Gilchrist A, Au CE, Hiding J, Bell AW, Fernandez-Rodriguez J, Lesimple S, Nagaya H, Roy L, Gosline SJ, Hallett M, Paiement J, Kearney RE, Nilsson T, Bergeron JJ. Quantitative proteomics analysis of the secretory pathway. Cell. 2006;127(6):1265–1281. [PubMed]
22. Ricci JE, Munoz-Pinedo C, Fitzgerald P, Bailly-Maitre B, Perkins GA, Yadava N, Scheffler IE, Ellisman MH, Green DR. Disruption of mitochondrial function during apoptosis is mediated by caspase cleavage of the p75 subunit of complex I of the electron transport chain. Cell. 2004;117(6):773–786. [PubMed]
23. Blackshaw S, Fraioli RE, Furukawa T, Cepko CL. Comprehensive analysis of photoreceptor gene expression and the identification of candidate retinal disease genes. Cell. 2001;107(5):579–589. [PubMed]
24. Joussen AM, Huang S, Poulaki V, Camphausen K, Beecken WD, Kirchhof B, Adamis AP. In vivo retinal gene expression in early diabetes. Invest Ophthalmol Vis Sci. 2001;42(12):3047–3057. [PubMed]
25. Gygi SP, Rochon Y, Franza BR, Aebersold R. Correlation between protein and mRNA abundance in yeast. Mol Cell Biol. 1999;19(3):1720–1730. [PMC free article] [PubMed]
26. Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, Eng JK, Bumgarner R, Goodlett DR, Aebersold R, Hood L. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science. 2001;292(5518):929–934. [PubMed]
27. Gao BB, Clermont A, Rook S, Fonda SJ, Srinivasan VJ, Wojtkowski M, Fujimoto JG, Avery RL, Arrigg PG, Bursell SE, Aiello LP, Feener EP. Extracellular carbonic anhydrase mediates hemorrhagic retinal and cerebral vascular permeability through prekallikrein activation. Nature medicine. 2007;13(2):181–188. [PubMed]
28. Gao BB, Chen X, Timothy N, Aiello LP, Feener EP. Characterization of the Vitreous Proteome in Diabetes without Diabetic Retinopathy and Diabetes with Proliferative Diabetic Retinopathy. Journal of proteome research. 2008;7(6):2516–2525. [PubMed]
29. Kowluru RA. Retinal metabolic abnormalities in diabetic mouse: comparison with diabetic rat. Curr Eye Res. 2002;24(2):123–128. [PubMed]
30. Sharon D, Blackshaw S, Cepko CL, Dryja TP. Profile of the genes expressed in the human peripheral retina, macula, and retinal pigment epithelium determined through serial analysis of gene expression (SAGE) Proceedings of the National Academy of Sciences of the United States of America. 2002;99(1):315–320. [PubMed]
31. Brownlee M. Biochemistry and molecular cell biology of diabetic complications. Nature. 2001;414(6865):813–820. [PubMed]
32. Barber AJ, Lieth E, Khin SA, Antonetti DA, Buchanan AG, Gardner TW. Neural apoptosis in the retina during experimental and human diabetes. Early onset and effect of insulin. The Journal of clinical investigation. 1998;102(4):783–791. [PMC free article] [PubMed]
33. Ejaz S. Importance of pericytes and mechanisms of pericyte loss during diabetes retinopathy. Diabetes Obes Metab. 2007;10(1):56–63. [PubMed]
34. Takamura Y, Tomomatsu T, Kubo E, Tsuzuki S, Akagi Y. Role of the polyol pathway in high glucose-induced apoptosis of retinal pericytes and proliferation of endothelial cells. Invest Ophthalmol Vis Sci. 2008;49(7):3216–3223. [PubMed]
35. Tewari M, Yu M, Ross B, Dean C, Giordano A, Rubin R. AAC-11, a novel cDNA that inhibits apoptosis after growth factor withdrawal. Cancer Res. 1997;57(18):4063–4069. [PubMed]
36. Morris EJ, Michaud WA, Ji JY, Moon NS, Rocco JW, Dyson NJ. Functional identification of Api5 as a suppressor of E2F-dependent apoptosis in vivo. PLoS Genet. 2006;2(11):e196. [PubMed]
37. Ju WK, Misaka T, Kushnareva Y, Nakagomi S, Agarwal N, Kubo Y, Lipton SA, Bossy-Wetzel E. OPA1 expression in the normal rat retina and optic nerve. J Comp Neurol. 2005;488(1):1–10. [PMC free article] [PubMed]
38. Wang AG, Fann MJ, Yu HY, Yen MY. OPA1 expression in the human retina and optic nerve. Exp Eye Res. 2006;83(5):1171–1178. [PubMed]
39. Alexander C, Votruba M, Pesch UE, Thiselton DL, Mayer S, Moore A, Rodriguez M, Kellner U, Leo-Kottler B, Auburger G, Bhattacharya SS, Wissinger B. OPA1, encoding a dynamin-related GTPase, is mutated in autosomal dominant optic atrophy linked to chromosome 3q28. Nat Genet. 2000;26(2):211–215. [PubMed]
40. Araujo H, Danziger N, Cordier J, Glowinski J, Chneiweiss H. Characterization of PEA-15, a major substrate for protein kinase C in astrocytes. J Biol Chem. 1993;268(8):5911–5920. [PubMed]
41. Renault F, Formstecher E, Callebaut I, Junier MP, Chneiweiss H. The multifunctional protein PEA-15 is involved in the control of apoptosis and cell cycle in astrocytes. Biochem Pharmacol. 2003;66(8):1581–1588. [PubMed]
42. Nagai N, Izumi-Nagai K, Oike Y, Koto T, Satofuka S, Ozawa Y, Yamashiro K, Inoue M, Tsubota K, Umezawa K, Ishida S. Suppression of diabetes-induced retinal inflammation by blocking the angiotensin II type 1 receptor or its downstream nuclear factor-kappaB pathway. Invest Ophthalmol Vis Sci. 2007;48(9):4342–4350. [PubMed]
43. Gilbert RE, Kelly DJ, Cox AJ, Wilkinson-Berka JL, Rumble JR, Osicka T, Panagiotopoulos S, Lee V, Hendrich EC, Jerums G, Cooper ME. Angiotensin converting enzyme inhibition reduces retinal overexpression of vascular endothelial growth factor and hyperpermeability in experimental diabetes. Diabetologia. 2000;43(11):1360–1367. [PubMed]
44. Horio N, Clermont AC, Abiko A, Abiko T, Shoelson BD, Bursell SE, Feener EP. Angiotensin AT(1) receptor antagonism normalizes retinal blood flow and acetylcholine-induced vasodilatation in normotensive diabetic rats. Diabetologia. 2004;47(1):113–123. [PubMed]
45. Kurihara T, Ozawa Y, Nagai N, Shinoda K, Noda K, Imamura Y, Tsubota K, Okano H, Oike Y, Ishida S. Angiotensin II Type 1 Receptor Signaling Contributes to Synaptophysin Degradation and Neuronal Dysfunction in the Diabetic Retina. Diabetes. 2008;57(8):2191–2198. [PMC free article] [PubMed]