Verification of Multimarkers for Detection of Early Stage Diabetic

Feb 1, 2013 - Verification of Multimarkers for Detection of Early Stage Diabetic ... Nonproliferative diabetic retinopathy (NPDR) is the early DR whic...
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Verification of Multimarkers for Detection of Early Stage Diabetic Retinopathy Using Multiple Reaction Monitoring Kyunggon Kim,†,∥ Sang Jin Kim,‡ Dohyun Han,†,∥ Jonghwa Jin,† Jiyoung Yu,† Kyong Soo Park,§ Hyeong Gon Yu,*,‡ and Youngsoo Kim*,†,∥ Departments of †Biomedical Engineering, ‡Ophthalmology, §Internal Medicine, and ∥Institute of Medical & Biological Engineering, Medical Research Center, Seoul National University College of Medicine, 28 Yongon-Dong, Seoul 110-799, Korea S Supporting Information *

ABSTRACT: Diabetic retinopathy (DR) is a complication of diabetes and 80% of diabetes mellitus (DM) patients whose DM duration is over 10 years can be expected to suffer with DR. The diagnosis of DR depends on an ophthalmological examination, and no molecular methods of screening DR status exist. Nonproliferative diabetic retinopathy (NPDR) is the early DR which is hard to be noticed in early NPDR, showing significant cause of adult blindness in type 2 diabetes patients. Protein biomarkers have been valuable in the diagnosis of disease and the use of multiple biomarkers has been suggested to overcome the low specificity of single ones. For biomarker development, multiple reaction monitoring (MRM) has been spotlighted as an alternative method to quantify target proteins with no need for immunoassay. In this study, 54 candidate DR marker proteins from a previous study were verified by MRM in plasma samples from NPDR patients in 3 stages (mild, moderate and severe; 15 cases each) and diabetic patients without retinopathy (15 cases) as a control. Notably, 27 candidate markers distinguished moderate NPDR from type 2 diabetic patients with no diabetic retinopathy, generating AUC values (>0.7). Specifically, 28 candidate proteins underwent changes in expression as type 2 diabetic patients with no diabetic retinopathy progressed to mild and moderate NPDR. Further, a combination of 4 markers from these 28 candidates had the improved specificity in distinguishing moderate NPDR from type 2 diabetic patients with no diabetic retinopathy, yielding a merged AUC value of nearly 1.0. We concluded that MRM is a fast, robust approach of multimarker panel determination and an assay platform that provides improved specificity compared with single biomarker assay systems. KEYWORDS: MRM, diabetic retinopathy, multimarker panel, biomarker, type 2 diabetes

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glycosylation5 and phosphorylation,6−8 and is applied to a wide range of samples, such as plasma,4,9 serum,10 vitreous11 and cerebrospinal fluid,12 feces,13 yeast,14 bacteria,15 and plant.16 Further, MRM, coupled with an enrichment method of stable isotope standards and capture by antipeptide antibodies (SISCAPA), improves the sensitivity of detection, overcoming the limitation that is imposed by the dynamic range of protein concentrations in clinical samples, such as plasma and serum.17,18 Many protein biomarkers have been used in research and in the clinic. For example, several tumor markers have been approved by the U.S. Food and Drug Administration (FDA), such as α-fetoprotein (AFP) for colorectal cancer;19 carcinoembryonic antigen (CEA) for testicular cancer;20 prostate-specific antigen (PSA) for prostate cancer;21 estrogen and progesterone receptor for breast cancer;22 CA 125 for cervical cancer; CA27.29 and CA 15-3 for breast cancer; and bladder tumor-

INTRODUCTION Early detection and timely treatment are the most effective means of curing a disease, for which biomarkers can be valuable. Biomarkers have been obtained from a wide spectrum of characteristics that can be measured objectively and evaluated as an indicator of normal or pathogenic processes and pharmacological responses to a therapeutic intervention.1 Clinically, biomarkers in these days provide information as a diagnostic tool, a stage-determining tool for disease, an indicator of disease prognosis, and a predictor and marker of clinical response, enabling the medical staff to make a treatment decision. Biomarker development can be divided into 4 stages discovery, qualification, verification, and validation2wherein multiple reaction monitoring (MRM) using triple quadrupole mass spectrometer has been applied in the verification and qualification stages.3 MRM allows multiplex peptide quantification in single runs using a known amount of stable isotopelabeled standard peptides as references.4 In addition, MRM is applied to quantify post-transcriptional modifications, such as © 2013 American Chemical Society

Received: October 21, 2011 Published: February 1, 2013 1078

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Figure 1. Overall experimental schemes. In the discovery of candidate markers, we performed 2 previous experiments. In the previous experiment 1, the vitreous proteomes of proliferative diabetic retinopathy (PDR) and macular hole (MH, nondiabetic control) patients were profiled, in which comparative proteome analysis represented the PDR-specific vitreous proteins.33 In previous experiment 2, individual vitreous samples from MH, PDR, and NPDR and their corresponding plasma were analyzed by MRM.40 The transitions of PDR-specific vitreous and plasma proteins were determined, and NPDR-specific candidate markers were proposed for the current MRM experiment. Candidate marker proteins from the 2 previous experiments were combined with SERPINs, apolipoproteins, and complement proteins that are associated with diabetes. The aim of the current experiment was to verify all candidate markers in distinguishing NPDR patients from type 2 diabetes patients with no diabetic retinopathy. Four stages of NPDR plasma samples (MI, MO, SV, NoDR) were used for the current MRM study. MRM data were subject to expression pattern and ROC curve analysis. Finally, the combination of multimarkers was proposed to formulate multimarker panel for the purpose of improving the performance of NPDR biomarkers.

loss in diabetic patients. Thus, with biomarkers that enable the early detection of DR, or nonproliferative diabetic retinopathy (NPDR), reduction of vision loss in diabetic patients can be expected. In this study, we collected plasma samples of NPDR patients according to 3 stages of NPDRmild (MI), moderate (MO) and severe (SV)and type 2 diabetic mellitus (T2DM) patients with no DR (NoDR) as a control group. We quantified candidate marker proteins using MRM in individual plasma samples. And after verification of protein markers for early stage of DR, classification analysis to make a panel in order to improve the specificity and sensitivity was tried.

associated antigen (BTA), nuclear matrix protein (NMP-22), and fibrin degradation product (FDP) for bladder cancer.23 These markers have been applied to get clinical information but there also have been concerns over their low specificity; thus, a diagnosis based on individual markers cannot be definitive. In contrast, a combination of numerous markers has been considered as an alternative approach of improving the low specificity of single markers. There have been several reports on assays that have used multimarker panels. Zhang et al. examined a panel of mRNA biomarkers (KRAS, MBD3L2, ACRV1, and DPM1) in saliva to detect pancreatic cancer, calculating a merged AUC of 0.921.24 Kim et al. showed that the combination of NNMT, FTL, and hNSE yielded the highest AUC (0.993) for the detection of renal carcinoma.25 In addition, several groups have used multimarker panels in biomarker discovery.26,27 Yet, no multimarker panel has been derived from data using multiple reaction monitoring (MRM), despite of its potentials as multiplexing quantitative tool. Diabetic retinopathy (DR), a microvascular complication of diabetes, can lead to acquired blindness.28,29 In the U.S., a recent epidemiological study estimated the prevalence of DR to be 28.5% among adults with diabetes mellitus (DM).30 In a Wisconsin-based epidemiological study of DR, the type 1 diabetes cohort had the 25-year cumulative incidence of DR, proliferative diabetic retinopathy (PDR, later stage of DR), and macular edema of 97%, 42%, and 29%, respectively.31,32 Despite good control of systemic risk factors, such as hyperglycemia, hypertension, and dyslipidemia, many DR patients progress to vision-threatening PDR. As the prevalence of diabetes rises, the number of patients with visual impairments that are caused by DR will climb. Nevertheless, early detection and timely treatment of DR can decrease the incidence of severe vision

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MATERIAL AND METHODS

Overall Experimental Schemes

An outline of this study is shown in Figure 1. Three independent experiments had been performed. Briefly, two kinds of candidate marker proteins were combined for this study: DR-specific expressed candidates from the 2 previous experiments and T2DM-associated plasma proteins. “Previous experiments 1 & 2” had been performed at the discovery stage as reported,33 in which several candidate marker proteins for DR were initially selected. In previous experiment 1, T2DM DR-specific vitreous proteins were listed from a comprehensive profile of human vitreous samples by LC−MS/MS, and in previous experiment 2, these T2DM DR-specific proteins were preliminarily verified as candidate markers by MRM in vitreous and corresponding plasma samples of DR patients. In addition, plasma proteins that had been reported as related to T2DM were added to the list of candidate markers for the current experiment. The combined list of candidate markers was used in the current MRM verification study 1079

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Proteins were denatured in 100 μL of 6 M urea for 1 h at room temperature (RT), and 1.7 μL of 200 mM DTT was added for 30 min to reduce disulfide bonds; this step was followed by alkylation with 6.7 μL of 200 mM IAA for 1 h in the dark. Five hundred microliters of DW was added to dilute the urea to below 1 M, and trypsin digestion was performed at a protein/trypsin ratio of 50:1. After an overnight incubation at 37 °C on a shaker, 50 μL of 0.1% TFA was added to quench the reaction. The digested peptide mixture was applied to a Sep-PAK tC18 cartridge (50 mg) to desalt it and eluted with 2 mL of 60% ACN with 0.1% TFA solution. After lyophilization, 200 μL of Sol A (98% DW, 2% ACN and 0.1% FA) was added to dissolve the desalted peptides. β-Galactosidase peptide (residues 954-962, GDFQFNISR) was added to 50 fmol in each peptide mixture as a relative internal standard peptide for MRM, as described.11

(“current experiment”) on 60 plasma samples from NoDR, MI, MO, and SV NPDR patients. After collection of relative quantitative information, further statistical analyses were performed as in method below. Reagents

Bicinchoninic acid (BCA, Cat. No: B9643) and copper(II) sulfate solutions (Cat. No: C2284) were obtained from Sigma (St. Louis, MO). Sequencing-grade modified trypsin (Cat. No: V5111, porcine) was purchased from Promega (Madison, WI). For protein digestion, iodoacetamide (IAA, Cat. No: I1149), dithiothreitol (DTT, Cat. No: 43815), urea (Cat. No: U6504), formic acid (FA, Cat. No: F0507), and trifluoroacetic acid (TFA, Cat. No: T62200) were purchased from Sigma. HPLCgrade methanol (MeOH, Cat. No: UN1230), acetonitrile (ACN, Cat. No: UN1648), and HPLC-grade water were obtained from Duksan (Seoul, Korea). Sep-PAK Vac (1 cc, tC18 cartridges, Cat. No: WAT054960) was purchased from Waters (Milford, MA) for peptide desalting. Peptide mixtures of β-galactosidase from Escherichia coli (Cat. No: 4333606) was purchased from Applied Biosystems (Foster City, CA). The PicoTip emitter (stock no: FS360-2010-N-20-C12, inner diameter (i.d.) in tip 10 μm, i.d. 20 μm, outer diameter (o.d.) 360 μm, length 20 cm) and IntegraFrit capillary (stock no: IF360-75-50-N-5, i.d. 75 μm, o.d. 360 μm, length 50 cm) were purchased from New Objective (Woburn, MA). Sample vials (part no: LP1114-1265, 0.2-mL glass microinsert, silanized) were purchased from Interface Engineering (Seoul, Korea). To minimize the absorption of a protein or peptide, we used Protein LoBind tubes (Cat. No: 022431081, 1.5 mL) and low-retention tips (Rainin Instrument, Oakland, CA).

Multiple Reaction Monitoring Using Triple Quadrupole Mass Spectrometer

MRM analysis was performed using a triple quadrupole linear ion trap mass spectrometer (4000 Qtrap coupled with nano Tempo MDLC, Applied Biosystems), as described.11 The sample loop in the autosampler was modified with 100-μm i.d. capillary tubing to hold 1.0 μL of sample to reduce void volume and obtain sharp intensity peaks. A homemade analytical column was built using an IntegraFrit capillary (i.d.: 75 μm, o.d.: 360 μm) and Magic C18AQ (200 Å, Michrom Bioresources, Madison, WI) to 15 cm in length. One microliter of plasma peptide mixture (1.0 μg of plasma) was injected directly into the analytical column without a trap column with Sol A (98% DW, 2% ACN, 0.1% FA) and Sol B (98% ACN, 2% DW, 0.1% FA). The flow rate was set to 300 nL/min, after which exponential gradient elution was performed by increasing the mobile phase from 0% to 40% Sol B over 60 min. The gradient was then ramped to 90% B for 10 min and 0% B for 10 min to equilibrate the column for the next run. A triple quadrupole mass spectrometer that was interfaced with a nanospray source was synchronized with nanoflow LC. The voltage for the ion spray was set to 2100 V, and the source temperature was 160 °C. The declustering potential (DP) was set to 80 V, curtain gas was set to 15, and collision gas (CAD) was set to high, corresponding to approximately 4−3 × 10−5 Torr. In the MRM mode, the resolutions at quadrupole part 1 (Q1) and quadrupole part 3 (Q3) are unit resolution. The collision energy (CE) for each transition was based on the results from preliminary runs, for which they were generally similar to theoretical values that were calculated from the equations CE = 0.044 * (m/z) + 8 for (M + 2H+) ions and CE = 0.030 * (m/z) + 8 for (M + 3H+) ions. In the MRM runs, the scan time was maintained at 15 ms for each transition, and the pause between transition scans was set to 5 ms. MRM runs preceded in 2 steps: the first MRM to determine transitions, and a second MRM to monitor target transitions for quantification. In the first MRM, after an enhanced mass scan (EMS) that ranged from 400 to 1200 m/z at a scan speed of 4000 Da/s, 2 enhanced product ion (EPI) scans were performed to obtain MS/MS information, followed by MASCOT search engine in in-house sequence database to identify the proteins.35 In the second MRM, selected target peptides were quantified without any information-dependent scan or MS/MS scan.

Plasma Samples

Plasma collection from T2DM patients was approved from the institutional review board (IRB) of Seoul National University Hospital (IRB approval No.: H-0807-086-251). Eight-hour fasting blood samples were collected from T2DM NPDR patients at the Department of Ophthalmology of Seoul National University Hospital.33 T2DM NPDR patients were categorized into 3 classesMI, MO, and SV NPDRper the international clinical diabetic retinopathy disease severity scale34 after the ophthalmological evaluation including a slit lamp biomicrosopic exam, dilated fundus exam, fundus photographs, and fluorescein angiography. Control (NoDR) bloods were collected from T2DM patients without retinopathy. Each plasma sample was processed per an established protocol.11 In brief, blood was collected in K2-EDTA-coated 10-mL tubes (BD Sciences, NJ, P/N number: 367525) and centrifuged at 3000g for 10 min at 4 °C. Each plasma sample was aliquoted in small volumes (50 μL) and kept at −80 °C. Maximum storage duration was 6 months. Preparation of Plasma for Mass Spectrometry

Plasma was prepared for mass spectrometry as described.11 In brief, the protein concentration in each plasma sample was determined by BCA method per the manufacturer. Each plasma sample was diluted initially 1:400 with distilled water (DW) to reduce pipeting errors; low-retention tips and low-binding tubes were used to minimize the adsorption of proteins or peptides to the tube surface. Two hundred micrograms of plasma was used, and the diluted plasma samples were reduced to approximately 10 μL by lyophilization. 1080

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Figure 2. DM duration and HbA1c level. Duration of type 2 diabetes mellitus (T2DM) and level of HbA1c were plotted in 60 individual plasma samples as circles. The ANOVA analysis (Supplementary Table 2) indicated that there were no significant variations among the 4 groups, except for (A) T2DM duration in NoDR versus MO NPDR. There were no significant variations among the 4 groups in (B) HbA1c level (Supplementary Table 2). Black circles are NoDR group, green ones are MI NPDR, blue ones are MO NPDR, and red ones are SV NPDR.

Reproducibility of MRM Analysis

Medcalc (MedCalc Software, Mariakerke, Belgium, version 11.4) was used to generate receiver operating characteristic (ROC) curves, interactive plots, and clustered plots. In logistic regression analysis to combine proteins, logistic regression embedded in MedCalc was used using “ENTER” method. Variables were entered if p-value is below 0.05 and removed if p-value is above 0.1. Classification cutoff value is set to be 0.5, leave-one out cross validation was performed, and error rate was calculated in each model to avoid the overfitting because the sample size was too small to divide between training set and test set. To check the multi colinearity, variance inflation factors and tolerances were calculated in IBM SPSS Statistics (version 20, Academic licensed) and Hosmer and Lemeshow test was done to check the model suitability.

Pooled plasma sample were divided into 3 vials to be 100 μg each and plasma sample vials were prepared independently from denaturation, reducing and alkylation using identical methods as described. After overnight (O/N) trypsinization, each peptide sample was desalted in independent batch and dried. After reconstitution with Sol A to be 1 μg/μL, 6 proteins at the range of low (complement factor B and L-selectin), middle (transthyretin and vitronectin) and high abundance (α1-antichymotrypsin and Kininogen-1) were monitored in MRM in a manner of triplicate at each vial. CV of peak area of triplicate result of 6 proteins in 3 set were analyzed to check the reproducibility.

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Relative Quantitational Linearity of MRM Based on a Internal Standard Peptide Normalization

Seven concentrations (100, 50, 25, 10, 5, 1, and 0.5 fmol) of heavy amino acid (arginine in C-terminus) labeled peptide of E. coli, GDFQFNISR (transition of 547.4/646.2), were spiked into peptide mixture of pooled plasma and 50 fmol of heavy amino acid (lysine in C-terminus) labeled peptide from E. coli LNVENPEK (transition of 411.3/594.4) was also spiked in each vial. Then, triplicate MRM analysis was done for each concentration and the subsequent response curve of GDFQFNISR peptide was plotted. Identical analysis was done using unlabeled peptides of GDFQFNISR and LNVENPEK.

RESULTS

Plasma Collection from Three Stages of NPDR

Fifteen fasting plasma samples were collected from the NoDR, MI, MO, and SV NPDR patient groups each. The mean age and gender distribution of the study group are shown in Supplementary Table 1. There were no significant differences with regard to age and gender (Supplementary Table 2). The highest averaged T2DM duration was observed for the MO NPDR group (15.9 years), and by ANOVA, there were differences in DM duration between the NoDR and MO NPDR groups (Figure 2, Supplementary Table 2). HbA1c level, a measure of the quantity of sugar molecules that reflects the average levels of blood glucose in the last 2−3 months, exceeded 7.4% in all groups and did not differentiate among the 4 groups by ANOVA (Supplementary Table 2 and Figure 2). And the proportion of patients with hypertension, insulin therapy, hyperlipidemia and diabetic nephropathy including microalbuminuria and proteinuria did not differ between groups (Supplementary Table 3). To avoid variations that are caused by additional preparation, high-abundance proteins, such as serum albumin and immunoglobulin, were not depleted.36 Undepleted plasma can suppress ion signals for target transition by matrix ions; but most target proteins in this study were moderate in abundance,

Statistical Analysis

MRM result files (wiff and wiff.scan) were imported into peak area integration software, MultiQuant (Applied Biosystems, version 1.0) to extract the peak areas of transitions and to normalize using the peak area of internal standard transitions (Q1/Q3 transitions as 542.3/636.3 m/z for the β-galactosidase peptide, GDFQFNISR) to adjust for variations between runs, as described.11 Gaussian smooth width was 1.0 points and retention time window was set as 30.0 s. In integration parameters, noise percentage was 40.0%, baseline subtraction window was 2.00 min and peak splitting was 2 points. Minimum peak width was 3 points. The adjusted peak areas of target transitions were assigned as MI, MO, SV, and NoDR. 1081

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Figure 3. Reproducibility of 3 independent MRM analyses. Six transitions corresponding to 6 proteins were monitored to check the reproducibility of MRM runs. Plasma peptide mixture samples were prepared as 3 independent sets from denaturation to desalting steps. Kininogen and α-1antichymotrypsin as high-abundance proteins, tranthyrectin and vitronectin as middle-abundance proteins and complement factor B and L-selectin as low-abundance proteins were monitored in triplicate manner for 3 independent samples, respectively (Set A, Set B and Set C). Here, three replicate data for the Set A are shown where the triplicate MRM runs for 6 transitions were shown as overlaid extract ion chromatogram. Blue, red, and black color lines represent the XIC of transitions from first, second, and third run, respectively. The peak area and CV% for all 3 sets are shown in Supplementary Table 4.

proteome of T2DM PDR and macular hole (MH, nondiabetic control) was profiled by LC−MS/MS, wherein 415 DR-specific proteins were identified after transproteomic pipeline (TPP) validation. In previous experiment 2, candidate marker proteins for DR were validated using MRM in vitreous and its corresponding plasma for 15 NPDR patients, 19 PDR patients, and 15 MH patients as control. From previous experiment 2, 31 candidate marker proteins for DR were selected including the 12 candidates for DR (thyroxine-binding globulin (TBG), hepatocyte growth factor activator (HGFA), gamma glutamyl hydrolase (gGH), kallistatin (KAL), von Willebrand factor (vWF), glyceraldehyde 3-phosphate dehydrogenase (GAPDH), coagulation factor IX (CoA), apolipoprotein B100 (APOB100),

generating detectable signal-to-noise ratios, even in nondepleted plasma status. Selection of Candidate Marker Proteins for MRM

We performed two kinds of independent methods in order to select the plasma proteins which can be marker candidates of DR. One trial is to use the proteins which have been discovered as marker for diabetic retinopathy in our previous studies. And the other one is using the plasma proteins which have been known to DM pathology in previous publication. This procedure for selection was described in Figure 1. The first group of candidate proteins was selected from previous experiments 1 and 2.11,33 In previous experiment 1, vitreous 1082

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Figure 4. Normalization of the peak areas of transitions using the standard peptide. (A) Blue circles represent the peak area of the β-galactosidase peptide as an internal peptide standard (residues 954−962 m/z, GDFQFNISR), while the shaded bars represent the peak area of transition for hemopexin (610.8/775.4 m/z) in 60 individual plasma samples. The peak area of the internal standard (542.3/636.3 m/z) is on the left vertical axes, while the peak area (hemopexin peptide) is on the right vertical axes. The sample group on the horizontal axis, NoDR, MI, Mo, and SV, represent 15 runs of each sample group. (B) The peak area of hemopexin (610.8/775.4 m/z, shaded bar) was normalized versus that of the internal standard (542.3/636.3 m/z, blue circle).

intensity among other transitions in a peptide was selected to analyze. Peak areas of each transition were extracted after MRM runs and normalized to the internal standard, 50 fmol βgalactosidase peptide, to adjust for run-to-run variations.40

myocillin (MYO), pigment epithelium-derived factor (PEDF), peroxiredoxin 2 (PRX2), and haptoglobin (HAP)) that constituted the primary list of proteins in the subsequent MRM verification experiment. The second group of candidate markers was selected from plasma proteome which had been reported to be related to T2DM pathology. Considering DR is a compliance of DM, T2DM related plasma proteins can be the candidates for DR. The goal of this study is the verification of early stage of DR biomarkers in plasma and starting the MRM analysis with as many candidates as possible is reasonable. Global searching for the previous publication indicated that 3 functional protein families showed the relation with T2DM; lipoproteins, complement, and serine protease inhibitors (SERPINs). Apolipoproteins, which function in lipid metabolism, have been proposed to mediate diabetes.37 Kako et al. reported the effects on lipoproteins and atherosclerosis on streptozotocininduced diabetes in human apolipoprotein B transgenic mice, and Davidson published a proteomic study of apolipoproteins in LDL subclasses for patients with metabolic syndrome and type 2 diabetes.38 Gao et al. established a relationship between complement components and diabetic retinopathy.39 Several SERPIN proteins are associated with diabetic retinopathy. Zhang et al. noted anti-inflammatory and antioxidant effects of SERPINA3K in the retina.13 Ultimately, 7 lipoproteins (Apolipoprotein A-I, Apolipoprotein A-II, Apolipoprotein AIV, Apolipoprotein B100, Apolipoprotein C-I, Apolipoprotein C-III, Apolipoprotein E), 7 complement components (Complement C3, Complement C4 alpha, Complement C4 beta, Complement C4 gamma, Complement C9, Complement factor B, Complement H), and 9 SERPIN proteins (α-1-antitrypsin, α-1-antichymotrypsin, α-2-antiplasmin, angiotensinogen, antithrombin-III, kallistatin, PEDF, plasma protease C1 inhibitor, thyroxine-binding globulin) formed the second group of candidate markers. Altogether, the 2 groups of candidates generated 54 markers for the current MRM experiment. The 144 transitions of the 67 peptides from 54 candidate proteins, plus an internal standard peptide, were determined, as shown in Supplementary Table 4. In further data analysis, the transition which showed the most

Reproducibility of MRM Runs and Normalization for Run-to-Run Variations

Plasma sample from single patient was divided into three vials (Set A, Set B, and Set C) and denatured, reduced, and alkylated, followed by trypsin digestion independently. Each vial was analyzed in triplicate using MRM mode. Specifically, 6 proteins were monitored in MRM to check the reproducibility at the range of low (complement factor B and L-selectin), middle (transthyretin and vitronectin) and high abundance (α1-antichymotrypsin and Kininogen-1), showing the CV of 3.4− 7.5% for the Set A in Figure 3 (CV of the Set B and C in Supplementary Table 5). This result showed that the reproducibility of MRM where targets are plasma proteins in wide range of concentration is reasonable for further MRM analysis. Fifty femtomoles of internal standard peptide (IS, GDFQFNISR from E. coli β-galactosidase) was added to each of the 60 samples while the transitions for IS (transition: 542.3/ 636.3) were monitored for 60 runs. During an MRM run, the spray status can fluctuate due to its instability, varying the peak area of each transition between runs. For reasonable relative quantitation based on this reproducibility, the peak area of the target transition was normalized to that of the IS peptide (GDFQFNISR, 542.3/636.3 m/z) to correct for run-to-run variations between runs as shown in Figure 4. The normalization using exogenous standard peptide can enable the relative quantitation with low CV and the improved quantitational linearity as shown in Supplementary Table 5. Quantitative Linearity of MRM Analysis

Quantitative linearity of MRM analysis was examined in the presence of plasma matrix. Internal standard peptide (GDFQFNISR, transition of 547.4/646.2, the residue R was isotope-labeled) which was used for normalization was spiked at 7 concentrations (100, 50, 25, 10, 5, 2.5, 1, and 0.5 fmol) in the 1.0 mg of plasma. Fifty fmol of another internal standard 1083

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Figure 5. Cluster plots of candidate marker transitions in the NoDR, MI, MO and SV NPDR groups. (A) The blue-lined box and circle are a box plot and individual normalized concentrations of candidate markers in the NoDR group, respectively. Violet, yellow, and green plots are those of MI, MO, and SV NPDR plasma in the same format, respectively. Normalized concentration is on the y-axes. Four representative proteins of each expression pattern are displayed; kallistatin which shows the increase in MI and MO NPDR, apolipoprotein C-III which decreases in MI and MO NPDR, apolipoprotein C-1 for an increase in DR, and apolipoprotein B100 for a decrease in MO NPDR. The cluster plots which contain all the 67 quantified peptides (54 proteins) in 4 groups (NoDR, MI, MO and SV NPDR) were shown in Supplementary Figure 2. (B) Clustered plots for MI NPDR-specific decreases (complement factor H) or increases (apolipoprotein A-I, prothrombin, and α-2-macroglobulin).

Table 1. Expression Pattern of Candidate Transitions between NoDR, MI, and MO NPDRa Uniprot ID

protein name

AUC

ratio of average (NoDR/MI/MO)

expression pattern

P43652 P02656 P00751 P04004 P19827 P04114 P02787 P00450 P00747 P25311 P01011 P01024 PO5155 P04217 POS603 P19652 P04196 P01009 P02652 PO6727 P02749 P06396 PO2654 P29622 P36955 P00734 P02647 P01023

Afamin Apolipoprotein CTEI Complement factor B Vitronectin Inter-alpha-trypsin inhibitor heavy chain Apolipoprotein B100 Transferrin Ceruloplasmin Plasminogen Zinc-alpha-2-glycoprotein Alpha-1-antichymotrypsin Complement C3 Plasma protease Cl inhibitor Alpha-1B-glycoprotein Complement factor H Alpha-1-acid-glycoprotein 2 Histidine-rich glycoprotein Alpha-1-antitrypsin Apolipoprotein All Apolipoprotein A-IV Beta-2-glycoprotein Gelsolin Apolipoprotein Cl Kallistatin Pigment epithelium-derived factor Prothrombin Apolipoprotein Al Alpha-2-macroglobulin

0.91 0.91 0.91 0.90 0.89 0.88 0.87 0.83 0.82 0.82 0.81 0.80 0.76 0.71 0.76 0.80 0.80 0.78 0.78 0.75 0.75 0.73 0.80 0.92 0.76 0.77 0.76 0.62

1.00:0.69:0.29 1.00:0.35:0:20 1.00:0.70:0.27 1.00:0.71:0.40 1.00:0.69:0.29 1.00:0.85:0.38 1.00:0.68:0.28 1.00:0.52:0.32 1.00:0.82:0.50 1.00:0.80:0.44 1.00:0.70:0.37 1.00:0.89:0.58 1.00:0.84:0.63 1.00:0.92:0.82 1.00:0.62:0.84 1.00:1.04:0.60 1:00:0:96:0:55 1.00:0.93:0.53 1.00:1.14:0.56 1.00:1.00:0.61 1.00:0.90:0.61 1.00:0.98:0.65 1.00:1.42:1.26 1.00:3.76:4.42 1.00:1.56:2.49 1.00:2.14:1.05 1.00:1.52:0.89 1.00:1.39:0.90

Decrease in MI and MO Decrease in MI and MO Decrease in MI and MO Decrease in MI and MO Decrease in MI and MO Decrease in MI and MO Decrease in MI and MO Decrease in MI and MO Decrease in MI and MO Decrease in MI and MO Decrease in MI and MO Decrease in MI and MO Decrease in MI and MO Decrease in MI and MO Decrease in MI and MO Decrease in MO Decrease in MO Decrease in MO Decrease in MO Decrease in MO Decrease in MO Decrease in MO Increase in MI and MO Increase in MI and MO Increase in MI and MO Increase in MI Increase in MI Increase in MI

a

Four categories of expression patterns are shown. The AUC values from the ROC curves and ratio of averaged relative quantities were calculated for each marker proteins. The expression patterns for all 28 potential marker proteins are described in Supplementary Figure 2. The ROC curve, interactive plots, and AUC values are described for all 28 marker proteins in Supplementary Figure 3. UniProt ID is the accession identification in the universal protein resource of each protein.

peptide from E. coli β-galactosidase (LNVENPEK, transition of 411.3/594.4) was spiked in every sample vial for spray

normalization. After MRM analysis, peak area of each concentration were extracted and plotted. As a result of 1084

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Figure 6. Interactive plots and ROC curves. ROC curves and interactive plot plots of 4 proteins (afamin, apolipoprotein C-III, complement factor B and kallistatin). Inside the interactive plots, the values of sensitivity and specificity are shown as “Sens” and “Spec” in % value. Blue circles represent normalized concentrations of each protein in NoDR versus MO NPDR. Inside the ROC curve, the AUC values of each potential marker are shown. The ROC curve and interactive plot of all the 28 proteins are presented in Supplementary Figure 3.

Particularly, expression patterns of candidate markers were grouped into 4 classes (Figure 5A). The protein group like apolipoprotein CIII showed the fold changes, which decreased in MI NPDR and more decreased in MO NPDR and the protein group like kallistatin showed the stepwise fold change increase in MI NPDR and MO NPDR. The protein group like apolipoprotein C1 increases both in MI NPDR and MO NPDR while expression of MI NPDR is higher than MO NPDR. The protein group like apolipoprotein B100 shows no significant expression change between NoDR and MI NODR but decreases in MO NPDR. Interestingly, expressions of prothrombin, apolipoprotein A-1 and α-2-macroglobulin increase in MI NPDR while there are no significant changes in MO NPDR versus NoDR (Figure 5B and Table 1). The symptom of MI NPDR is not detected easily without a thorough ophthalmological examination. Thus, complement factor H (down regulated in MI NPDR) and prothrombin, apolipoprotein A-I, and α-2-macroglobulin (upregulated in MI NPDR) could be useful specifically for classifying mild-stage NPDR (Figure 5B). Particularly, 18 proteins where expressions decrease (15 proteins) or increase (3 proteins) both in MI and MO NPDR compared to NoDR group can be the indicators of NPDR (Supplementary Figure 2).

linearly regression analysis, response curve showed the linearity (R2 = 0.954) while the CV % were considerably high in low concentration points (32.9% and 30.0%, especially in 0.5 and 1.0 fmol points, respectively). We found that considerable errors exist at the low concentration points (Supplementary Figure 1A). After normalization using another internal standard peptide (LNVENPEK, transition of 411.3/594.4), R2 became 0.991 while CV % was below 18.1%. Errors were below 18% at the low concentration points (Supplementary Figure 1B). This indicates that relative quantitation using the bacterial exogenous internal standard (GDFQFNISR from E. coli βgalactosidase) would be an applicable economical option in the fast high-throughput verification experiment. Analysis of Expression Patterns

The clinical diagnosis of SV NPDR can be made easily due to its worsening symptomsthe deterioration of eyesight becomes more evident, making a diagnosis of severe NPDR that is based on plasma biomarkers less meaningful. Further, the protein expression patterns in the SV NPDR group differed compared with the MI and MO NPDR groups, which is less informative in this study, the aim of which is to discover protein markers for the early stages of DR. Thus, the SV NPDR group was excluded from further analysis, whereas its quantitative results are shown for proteins as cluster plots in Figure 5 and Supplementary Figure 2. Cluster plot analysis showed that expressions of 28 proteins were significantly meaningful on diagnosing NPDR (Supplementary Figures 2 and 3). The fold changes were examined for the averaged relative quantities of each protein in order to find its expression pattern among groups. The normalized peak areas of representative transitions for the above 4 patterns were plotted as clustered box-shaped plots by groups (NoDR, MI, MO and SV NPDR), as shown in Supplementary Figure 2.

Combination of Multiprotein Markers Improves Discriminatory Power

ROC curves and interactive plots were constructed for the 28 selected candidate markers (Supplementary Figure 3), 27 of which showed good discriminatory power for NPDR versus NoDR patients, with an AUC > 0.7, except for α-2macroglobin, which had an AUC of 0.622 (Table 1). ROC curves and interactive plots of representative marker proteins are shown in Figure 6. 1085

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Figure 7. Merged ROC curves in NoDR versus MI or MO NPDR. (A) Four potential marker proteins that showed an MI NPDR-specific increase/ decrease (α-2-macrogloblulin, prothrombin, complement factor B, and apolipoprotein A-I) were combined, and the merged ROC curve using the 4 marker combination was overlain on ROC curves of the 3 individual proteins. The AUC value of each protein and the 4-protein combination are shown inside the ROC curve. (B) Four potential marker proteins that showed a linearly increase/decrease (apolipoprotein C-III, complement factor B, afamin, kallistatin) from NoDR to MO NPDR were combined, and the merged ROC curve using the 4-marker combination was overlain on ROC curves of 3 individual proteins. The AUC values of each protein and the 4-protein combination are shown inside the ROC curve. Details of statistical results were shown in Supplementary Table 5.

could affect the quantitative results. To minimize the clinical risk factors, it would be necessary to use larger and diverse clinical sample population.

The combination of 4 MI NPDR-specific proteins complement factor H, prothrombin, apolipoprotein A-I, and α-2-macroglobulinshowed improved discriminatory power in distinguishing MI NPDR from NoDR, resulting in a merged AUC of 0.902 (Figure 7A). Further, to differentiate NoDR from MO NPDR, we selected 4 proteins whose expression fold changed increased or decreased in MI NPDR and MO NPDR. The 4-marker combination panel of afamin, apolipoprotein CIII, complement factor B, and kallistatin differentiated NoDR from MO NPDR with a merged AUC value of 1.0 (Figure 7B). All statistical detail results were shown in Supplementary Table 6.

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Four-Marker Panel for MI NPDR Diagnosis

MI NPDR is an early stage of NPDR where the biomarker which can differentiate the MI NPDR from NoDR group would be very useful. Four proteins (complement factor H, prothrombin, apolipoprotein A-I, and α-2-macroglobulin) increased or decreased preferentially in MI NPDR but did not change between NoDR and MO NPDR. By logistic regression analysis, the 4-marker panel correctly classified 13 NoDR patients and 12 MI NPDR patients from the 30 cases comprising 15 NoDR and 15 MI NPDR subjects, an accuracy of 83.3% and an accuracy of 76.7% with leave-one-out cross validation, showing no colinearity and significant level of p = 0.6927 (Supplementary Table 6A). This result demonstrates that the 4-marker panels can distinguish MI NPDR patients among type 2 diabetic patients with significant specificity. The merged AUC value was 0.902 with the 4-marker panel. Although a comprehensive statistical analysis should be performed to generate a more precise classification using larger number of clinical sample set, this study may represent that MRM-based multimarker panels are promising in the future study of classifying NPDR.

DISCUSSION

MRM Is an Efficient Multiplex Assay Platform

MRM is a platform suitable for multimarker assays of clinical samples, such as plasma and serum. In this study, 60 plasma samples from 4 groups (NoDR, MI, MO, and SV NPDR) were analyzed by triple quadrupole mass spectrometry, coupled with nanoflow liquid chromatography, in which 144 transitions that represented 54 candidate proteins with an internal standard peptide were quantified by high-throughput multiplex MRM. Antibody-based platforms, such as ELISA, Western blot, and Luminex systems, are rather expensive and time-consuming in the large verification step compared to our MRM-based verification using fast economical relative quantitation using the exogenous bacterial peptide. In fact, the multiprotein marker assay has not been developed extensively, partly due to expensive resources, for example, ELISA and Luminex.41 As a fast economical alternative, MRM of clinical samples might be used as an early platform to compensate for the limitations of antibody-based assay, thanks to to its high-throughput multiplexing of quantification. Further, we need to consider that there would be uncontrollable clinical characteristics which

Four-Marker Panel for MO NPDR Diagnosis

Proteins that are up- or down-regulated in state-specific manner can be more promising biomarkers. By logistic regression, we noted that a combination of 4 markers (afamin, apolipoprotein C-III, complement factor B, and kallistatin) correctly classified 15 NoDR patients and 15 MO NPDR patients from 30 subjects comprising 15 NoDR and 15 MI NPDR subjects. The merged AUC of the 4-marker panel was 1.00, demonstrating 100% accuracy and 90.0% accuracy in a cross-validated model 1086

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Consequently, the discriminatory power of the 4-marker panels for MI NPDR and MI + MO NPDR subjects versus NoDR patients was greater than that of single protein markers. Although such combinations can be improved by advanced statistical analysis, they showed to be the optional platform for early DR diagnosis, because there are no protein markers for classification of early DR using NDR patient plasma. The multiplexing capability of MRM of clinical samples can be used to verify and validate candidate makers and construct multimarker panels to compensate for the low specificity and sensitivity of single maker diagnostic methods, such as antibody-based assays. If MRM-based diagnostic approaches can be improved for use in the clinic, they can surpass immunoassay-based systems with regard to high throughput, multiplexing, low cost, and obviating antibody preparation during assay development. Currently, generating a comprehensive prediction model is underway using advanced statistical analysis.

(Supplementary Table 6B), a considerable expectation, given that all 4 markers had AUC values >0.9. In addition, the 4-marker panel correctly identified 13 NoDR and 26 DR patients (out of 45 cases, comprising 15 MI NPDR, 15 MO NPDR, and 15 NoDR patients), demonstrating 86.7% accuracy in an initial model and in a cross-validated model and a merged AUC value of 0.933. There was no multicolinearity in the model, showing the variance inflation factor (VIF) value below 3 (Supplementary Table 6C). This prediction model, which is composed of 4-proteins, can be improved if advanced statistical analysis methods such as multivariate analysis are applied to the 28 up- and down-regulated candidate markers. Association of Markers in Type 2 Diabetes and Diabetic Retinopathy

Kallistatin as one of SERPIN is a specific inhibitor of tissue kallikrein and maintains the avascular status of vitreous bodies. In a previous study, we observed that kallistatin expression increased in NPDR versus nondiabetic controls in vitreous and plasma, which was confirmed in the current report.40 Ogata et al. noted that pigment epithelium-derived factor, a robust inhibitor of angiogenesis, was significantly elevated in the plasma of diabetic patients, especially those with proliferative diabetic retinopathy, whereas we observed a consistent expression pattern.42 Hirano et al. reported that apolipoprotein CIII and apolipoprotein C1 increased in diabetic nephropathy, whereas both proteins decreased in early diabetic retinopathy in our study, which might be attributed to different quantitation methods that were used.43 Alpha-1-acid glycoprotein, an acute-phase reactant, rises in the vitreous during proliferative diabetic retinopathy39 and increased in plasma in early grade NPDR in this study. Elder et al. reported that antithrombin III is upregulated in PDR plasma versus nonretinopathy and control groups,44 whereas Gao et al. observed a 2-fold increase in vitreous antithrombin III in PDR patients compared with NoDR subjects.39 In contrast, antithrombin III decreased slightly in early grade NPDR plasma compared with NoDR controls, which might be attributed to differences in plasma samples from NPDR and PDR patients. Celuroplasmin is a major copper-carrying protein and mediates iron metabolism;45 plasminogen is converted to the active enzyme plasmin by urokinase and tissue plasminogen activator. No report has linked celuroplasmin and plasminogen with diabetic retinopathy, but their combination has high power in distinguishing NPDR from NoDR patients.

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ASSOCIATED CONTENT

S Supporting Information *

(1) Supplementary Table 1: Characteristics of the clinical samples in the 4 groups. (2) Supplementary Table 2: Statistics for clinical sample groups. (3) Supplementary Table 3: Statistical analysis for patient characteristics. (4) Supplementary Table 4: Table of MRM transitions. (5) Supplementary Table 5: Reproducibility of MRM runs. (6) Supplementary Table 6A: Statistics for logistic regression in NoDR versus MI NPDR using the 4- marker panel. (7) Supplementary Table 6B: Statistics for logistic regression in NoDR versus MO NPDR using 4- marker panel. (8) Supplementary Table 6C: Statistics for logistic regression in NoDR versus MI + MO NPDR using 4-marker panel. (9) Supplementary Figure 1: Standard curve of internal standard peptide. (10) Supplementary Figure 2: Cluster plots of quantified candidate markers for 4 patient groups. (11) Supplementary Figure 3: Interactive and ROC curves for 28 potential markers. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*(Y.K.) Address: Department of Biomedical Engineering, Seoul National University College of Medicine, 28 Yongon-Dong, Chongno-Ku, Seoul 110-799, Korea. Tel: +82-2-740-8073, Fax: +82-2-741-0253. E-mail: [email protected]. (H.G.Y.) Address: Department of Ophthalmology, Seoul National University College of Medicine, 28 Yongon-dong, Chongno-gu, Seoul 110-744, Korea. Tel: +82- 2-760-2438. Fax: +82- 2-741-3187. E-mail: [email protected].

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CONCLUSION Fifty-four candidate markers, which were discovered in previous studies, were quantified by MRM in 60 plasma sample sets, comprising 15 mild, 15 moderate, and 15 severe NPDR patients and 15 type 2 diabetes patients without retinopathy as controls. The quantified MRM data were normalized against an internal standard peptide and analyzed to determine the expression patterns of the candidate markers to select early biomarkers for NPDR. As a result, 28 potential markers were differentially expressed among NoDR, MI, and MO NPDR patients. ROC curves were plotted to examine the discriminatory power of the selected markers in distinguishing MI NPDR or MO NPDR subjects from NoDR patients. In particular, 2 separate 4-marker panels stratified MI NPDR and MI + MO NPDR patients, respectively, against NoDR patients.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the National Research Foundation of Korea grant funded by the Korea government [MESF] (No. 2011-0030740) and the Proteogenomic Research Program through the National Research Foundation of Korea funded by the Korean Ministry of Education, Science and Technology. 1087

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