Decoding the S-Nitrosoproteomic Atlas in Individualized Human

Article pubs.acs.org/jpr

Decoding the S‑Nitrosoproteomic Atlas in Individualized Human Colorectal Cancer Tissues Using a Label-Free Quantitation Strategy Yi-Ju Chen,†,‡ Wei-Chieh Ching,†,§ Jinn-Shiun Chen,∥,⊥ Tzong-Yi Lee,# Cheng-Tsung Lu,# Hsiao-Chiao Chou,† Pei-Yi Lin,† Kay-Hooi Khoo,‡,○ Jenn-Han Chen,∇ and Yu-Ju Chen*,† †

Institute of Chemistry, Academia Sinica, Taipei, Taiwan Institute of Biochemical Sciences, National Taiwan University, Taipei, Taiwan § Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan ∥ Colorectal Section, Department of Surgery, Chang Gung Memorial Hospital, Taoyuan, Taiwan ⊥ School of Medicine, Chang Gung University, Taoyuan, Taiwan # Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan ○ Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan ∇ Translation Medicine Lab, Cancer Center, Wan-Fang Hospital, Taipei, Taiwan ‡

S Supporting Information *

ABSTRACT: The abnormal S-nitrosylation induced by the overexpression and activation of inducible nitric oxide synthase (iNOS) modulates many human diseases, such as inflammation and cancer. To delineate the pathophysiological Snitrosoproteome in cancer patients, we report an individualized S-nitrosoproteomic strategy with a label-free method for the site-specific quantification of S-nitrosylation in paired tumor and adjacent normal tissues from 11 patients with colorectal cancer (CRC). This study provides not only the first endogenous human S-nitrosoproteomic atlas but also the first individualized human tissue analysis, identifying 174 Snitrosylation sites in 94 proteins. Fourteen novel S-nitrosylation sites with a high frequency of elevated levels in 11 individual patients were identified. An individualized S-nitrosylation quantitation analysis revealed that the detected changes in S-nitrosylation were regulated by both the expression level and the more dramatic post-translational S-nitrosylation of the targeted proteins, such as thioredoxin, annexin A4, and peroxiredoxin-4. These endogenous S-nitrosylated proteins illustrate the network of inflammation/cancer-related and redox reactions mediated by various S-nitrosylation sources, including iNOS, transnitrosylase, or iron−sulfur centers. Given the demonstrated sensitivity of individualized tissue analysis, this label-free approach may facilitate the study of the vastly under-represented S-nitrosoproteome and enable a better understanding of the effect of endogenous S-nitrosylation in cancer. KEYWORDS: S-nitrosylation, biotin switch, label-free quantitation, colorectal cancer

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INTRODUCTION Protein S-nitros(yl)ation is a reversible post-translational modification in which nitric oxide (NO) is covalently attached to the sulfur of a cysteine residue. Given the increasing number of proteins reported to be regulated by this modification, Snitrosylation is considered to act, in a manner analogous to phosphorylation, as a pleiotropic regulator that elicits dual effects to positively or negatively regulate diverse physiological or pathophysiological processes by altering protein function, stability, and conformation1−4 in cardiovascular disease and protection,5 neurodegenerative disease,6,7 the immune response, 8 inflammation, 9 and cancer.10 While low NO concentrations maintain a normal cellular activation signal, increased NO levels due to the overexpression of inducible nitric oxide synthase (iNOS) may lead to the S-nitrosylation of © 2014 American Chemical Society

cellular components and the progressive development of severe inflammation and carcinogenesis.11 The magnitude and chronicity of NO generation are regulated by iNOS in activated macrophage and tumor cells via S-nitrosylation signaling, providing a plausible link between inflammation and cancer progression,12−16 including oral,17 pancreatic,18 stomach,19 and colon cancers.20,21 Local inflammation induced by NO or present in tissues colonized by bacteria also triggers diverse abnormalities in Special Issue: Proteomics of Human Diseases: Pathogenesis, Diagnosis, Prognosis, and Treatment Received: March 18, 2014 Published: July 20, 2014 4942

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cancer.10,22 In colorectal cancer (CRC), one of the most prevalent cancers and the third leading cause of cancer mortality worldwide,23 at the molecular level, the inflammatory response is accompanied by iNOS overexpression and NO production, leading to the activation of S-nitrosylation signaling in tumors, whereas iNOS expression is low or absent in the surrounding normal tissues.11,21,22,24,25 In CRC, most of the reported S-nitrosylated protein targets were identified in response to exogenous NO donors in colon cancer cell lines.26,27 Thus, the direct targets of endogenous iNOS-induced S-nitrosylation and their site-specific changes in modification stoichiometry in CRC remain unidentified. The potential link between the S-nitrosylation network in the endogenous Snitrosoproteome and the pathological development of CRC also merits further study. The identification of these CRCresponsive S-nitrosylated proteins in vivo, such as in patient tissue, may provide a potential drug target to inhibit the growth of cancer cells. However, due to the labile nature of this modification and analytical challenges, the in vivo protein targets of S-nitrosylation and their abnormal states in disease have not been systematically explored, particularly in cancer, where NO plays multifaceted, concentration-dependent roles. Therefore, measuring the reversible and dynamic changes in protein Snitrosylation may shed light on the protein targets associated with specific physiological disorders and S-nitrosylationmediated molecular mechanisms. In addition, the cellular effect of protein S-nitrosylation is multifaceted in terms of its dependence on concentration and subcellular localization, and the unbiased quantitation of protein S-nitrosylation from a systems biology perspective is therefore critical to correlate the dynamics and degree of S-nitrosylation with physiology or pathophysiology. Stable isotope labeling approaches, such as cICAT,28 SNOCAP,29 cysTMT,30 iTRAQ,31 and SILAC,32,33 have been successfully used to quantify the abundance of protein Snitrosylation in systems involving various cell lines. As an alternative to conventional stable isotope labeling approaches, label-free strategies have rapidly evolved for application in quantitative proteomics due to the advantage of having simple, cost-effective, and unlimited sample sets for various materials. Few studies have utilized the peak area from the extracted ion chromatogram in the LC−MS/MS profile to quantify the level of S-nitrosylated peptides in S-nitrosoglutathione-treated myocardia or the hearts of mice subjected to ischemia/ reperfusion injury.34,35 An alternative label-free method, a spectrum-counting approach that quantifies proteins based on the number of MS/MS spectra assigned to each protein, has been reported for quantifying S-nitrosylated proteins in LPS/ IFNγ-induced RAW264.7 macrophage cells.36 Although it offers the advantage of simplicity in computation, this method may have lower quantification accuracy for PTM analysis when the S-nitrosylated peptide has a limited number of spectral counts. To facilitate the large-scale analysis of the under-represented disease S-nitrosoproteome in vivo, here we present a multiplexed label-free quantitative S-nitrosoproteomic strategy for the site-specific identification and quantitation of individualized S-nitrosoproteomic profiles in human cancerous tissues. We previously reported an S-alkylating biotin switch method to improve detection sensitivity and false-positive identification in S-nitrosoproteomic analysis.37 To develop a simple label-free strategy for high-performance quantitative S-nitrosoproteomics,

in this study, we further combined the S-alkylating biotin switch method with an informatics-based peptide cross-alignment approach.38 This strategy was designed to increase the number of quantifiable S-nitrosylated peptides and to enhance the quantitation accuracy and precision. The quantitation sensitivity, accuracy, and reproducibility of the label-free strategy were first evaluated on triplicate cell lysates. Using CRC as a cancer model, this strategy was applied to analyze the differentially expressed S-nitrosoproteins in paired tumor and adjacent normal tissues from 11 individual CRC patients, followed by validation of selected proteins in 24 patients. In contrast to previous reports, this study is the first to systematically explore endogenous S-nitrosylated proteins from human cancer tissue in vivo, which may facilitate the discovery of potential targets that regulate human CRC. We also hope that this first mapping of individual S-nitrosoproteomic patterns among patients may permit an evaluation of the biological heterogeneity among individuals.

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EXPERIMENTAL PROCEDURES

Materials

Trypsin (modified, sequencing grade) was obtained from Promega (Madison, WI). The bicinchoninic acid (BCA) protein assay kit, Bradford protein assay kit, EZ-Link iodoacetyl-PEG2-biotin, and Zeba desalt spin columns were purchased from Pierce (Rockford, IL). Sodium dodecyl sulfate (SDS), Tris, and urea were acquired from Amersham Biosciences (Uppsala, Sweden). Acetone, HPLC-grade acetonitrile (ACN), ethanol, ethylenediaminetetraacetic acid (EDTA), and methanol were purchased from Merck (Darmstadt, Germany). L-Cysteine, dithiothreitol (DTT), glycerol, iodoacetamide (IAM), neocuproine, 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), β-mercaptoethanol, phosphate-buffered saline (PBS), phenylmethylsulfonyl fluoride (PMSF), potassium chloride (KCl), sodium chloride (NaCl), sodium ascorbate, Tris (2-carboxyethyl)-phosphine hydrochloride (TCEP-HCl), trifluoroacetic acid (TFA), Triton X100, Tween-20, sodium phosphate (NaH2PO4), and chicken ovalbumin (OVA) were obtained from Sigma-Aldrich (St. Louis, MO). Formic acid (FA) was purchased from Riedel de Haen (Seelze, Germany). Water was purified using a Milli-Q ultrapure water purification system (Millipore, Billerica, MA). S-Nitroso-N-acetylpenicillamine (SNAP), Nonidet P-40 (NP40), and protease inhibitor cocktail set III were obtained from Calbiochem (La Jolla, CA). Cell Culture

The human adenocarcinoma cell line CC-M1 was established from colon cancer tissues from Chinese patients39 and grown at 37 °C under 5% CO2 in Dulbecco’s modified Eagle’s medium (DMEM; HyClone, Logan, UT) supplemented with 10% heatinactivated fetal bovine serum (FBS; Invitrogen, Gaithersburg, MD) and a 1% antibiotic−antimycotic solution (Invitrogen). Patients and Specimen Collection

The project was registered and reviewed by the Institutional Review Board (IRB) on Biomedical Science Research in Taiwan. Clinical tissue samples were obtained from Chang Gung Memorial Hospital in Lin-Kou, Taiwan, in accordance with approved human subject guidelines authorized by the Medical Ethics and Human Clinical Trial Committee at Chang Gung Memorial Hospital. All CRC patients had histologically verified adenocarcinoma of the colon or rectum, as confirmed 4943

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Proteolysis of S-Nitrosylated Proteins by Trypsin

by pathologists. Patient characteristics were obtained from pathology records. Subjects with a history of other malignant diseases or infectious disease or who had undergone surgery within 6 months prior to the start of this research were excluded from this retrospective study. Following surgery, the tumor and adjacent normal tissues were collected in separate tubes, stored on dry ice for 30 min during transportation, and stored under liquid nitrogen before further processing and proteomic experiments. The adjacent normal tissue was obtained from the distal edge of the resection ≥10 cm from the tumor. In the discovery phase, 11 pairs of cancerous and adjacent normal tissue were collected and analyzed from individual patients with Dukes’ A (n = 2), Dukes’ B (n = 4), Dukes’ C (n = 3), or Dukes’ D (n = 2) stage of CRC (Supporting Information Table S1). In the validation phase, a total of 24 colorectal carcinoma patients were included. The clinical information on the patients is detailed in Supporting Information Table S1.

The biotinylated protein pellets from the last step of the biotin switch were resuspended in profiling buffer (50 mM Tris at pH 8.3, 0.1% (w/v) SDS, 0.02% (w/v) Triton X-100, and 2 M urea), and the protein concentration was adjusted to 2 mg/mL. The protein mixture was reduced by incubation with 5 mM TCEP for 30 min at 37 °C in the dark, and the reduced protein mixture was then S-alkylated with 3.5 mM IAM at 37 °C for 2 h in the dark. To quench the residual IAM, a 10-fold molar ratio of DTT relative to the initial molar ratio of IAM was added to the protein mixture, which was further incubated at room temperature for 5 min. The protein mixture was diluted with Milli-Q water to a final concentration of 1 M urea, digested with trypsin (1:25 w/w; trypsin/proteins) at 37 °C for 16 h, and dried completely under vacuum. Affinity Purification of S-Nitrosylated Proteins/Peptides

Biotinylated proteins or digested peptides were resuspended in avidin loading buffer (2 PBS, 40 mM NaH2PO4 at pH 7.2 and 300 mM NaCl), and the concentration was adjusted to 2 mg/ mL. The streptavidin agarose (Sigma-Aldrich) was first equilibrated by washing three times with 1 mL of avidin loading buffer. The resuspended samples were then mixed with the equilibrated streptavidin agarose beads at a ratio of 1:10 (v/ w, streptavidin agarose/sample) and incubated at room temperature for 1 h with gentle agitation. The supernatant was removed by centrifugation at 1000 rpm for 10 min. The beads were washed once with 1 mL of avidin washing I buffer (1× PBS at pH 7.2), followed by 1 mL of avidin washing II buffer (50 mM ABC at pH 8.3 and 20% methanol) and 1 mL of Milli-Q water. The biotinylated proteins or peptides were eluted in 1 mL of avidin eluting buffer (30% ACN and 0.1% TFA) at room temperature for 10 min with vigorous agitation. The purified biotinylated proteins were then resuspended in sample buffer and separated by SDS-PAGE for western blot analysis. The biotinylated peptides were desalted by C18 Zip-tip (Millipore) and subjected to downstream MS analysis.

Preparation of Cellular Extracts

Total cell lysates from CC-M1 cells were washed three times with PBS and disrupted in 300 μL of lysis buffer containing HEN buffer (250 mM HEPES at pH 7.7, 1 mM EDTA, and 0.1 mM neocuproine), 1% NP-40, 150 mM NaCl, 1 mM PMSF, and 1:100 (v/v) protease inhibitor cocktail set III. The frozen tissues were thawed rapidly at 37 °C, cut into small pieces, and washed with 0.9% NaCl to remove blood. The precleaned tissues were frozen in liquid nitrogen, ground, and lysed using lysis buffer containing 20 mM IAM and 1:100 (v/v) protease inhibitor cocktail set III. Cellular debris was removed via centrifugation at 16 000g at 4 °C for 30 min. The protein concentration of the remaining supernatant (the total cell lysate) was determined using the BCA protein assay kit. SNAP-Induced Protein S-Nitrosylation

For in vitro protein S-nitrosylation of standard proteins, bovine serum albumin (BSA) and ovalbumin (OVA) were disssolved in lysis buffer and incubated with a final concentration of 1 mM SNAP at 37 °C for 1 h in the dark. The excess SNAP was sequentially removed using Zeba desalt spin column tubes after removing the storage buffer and washing three times with HEN buffer. The recovery of SNO-BSA and SNO-OVA was quantified via the Bradford assay.

LC−MS/MS Analyses

All LC−ESI−MS/MS analyses were performed in a Q-TOF Premier mass spectrometer (Waters, Milford, MA) as previously described.37 Desalted biotinylated peptides were resuspended in buffer A (0.1% FA) and injected into a 2 cm × 180 μm capillary trap column. The peptides were then separated in a 75 μm × 20 cm nanoACQUITY UPLC BEH C18 column packed with 1.7 μm particles (Waters) using the nanoACQUITY ultra performance LC system (Waters). The column was maintained at 35 °C, and bound peptides were eluted with a linear gradient of 0−80% buffer B (0.1% FA in ACN) for 80 or 120 min at 300 nL/min. The MS was operated in ESI positive V mode with a resolving power of 10 000. A NanoLockSpray source was used to obtain accurate mass measurements, and the lock mass channel was sampled every 30 s. The mass spectrometer was calibrated with a synthetic human [Glu1]-fibrinopeptide B solution (1 pmol/μL, from Sigma-Aldrich) delivered through the NanoLockSpray source. Data acquisition was performed using the data-directed analysis (DDA) method. The DDA method involved one full MS scan (m/z 400−1600, 0.6 s) and three MS/MS scans (m/z 100− 1990, 1.2 s each scan) performed sequentially on the three most intense ions present in the full scan mass spectrum.

S-Alkylating Biotin Switch Method

S-Nitrosylated proteins were labeled by our previously developed S-alkylating biotin switch method.37 In brief, Snitrosylated bovine serum albumin (SNO-BSA) and ovalbumin (SNO-OVA) proteins were first spiked into the total lysate of each sample. The free cysteine thiols on the proteins were Salkylated by adding three volumes of blocking buffer (270 mM IAM in HEN buffer plus 5% (w/v) SDS) to the protein mixture and incubating at 37 °C for 2 h in the dark. After acetone precipitation to remove excess IAM, the mixture was centrifuged at 16 000g for 20 min. The protein pellet was then washed three times with 95% ice-cold ethanol and resuspended in HEN buffer with 1% SDS. The protein concentration was determined using the BCA protein assay kit and adjusted to 2 mg/mL. To label the S-nitrosothiols, the protein mixture was simultaneously reduced and labeled in HEN buffer containing 1:50 volume of 250 mM sodium ascorbate and 1:3 volume of 8 mM EZ-Link iodoacetyl-PEG2biotin at 37 °C for 2 h in the dark. Acetone precipitation was performed to remove excess sodium ascorbate and biotin. 4944

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Protein Identification

results of the differentially expressed S-nitrosylated peptides were further validated manually. The relative quantitative ratio was determined by the sample abundance of the corresponding peptide.

The peak list resulting from the MS/MS spectra was exported to mgf format using Mascot Distiller v2.1.1.0 by the following criteria: the precursor peak charge range on MS and MS/MS processing was 2 to 5; the precursor mass range was 400−4000 and the precursor m/z tolerance was 0.3; S/N ≥ 3. The data sets were batch-searched and combined searched with Mascot v2.2 (Matrix Science, London, United Kingdom) against the SwissProt database (v57.8, total 509 019 sequences, 20 329 of which are Homo sapiens) using the following constraints: only tryptic peptides with up to two missed cleavage sites were allowed, and the mass tolerance for both peptides and MS/MS fragment ions was 0.1 Da. PEO-iodoacetyl-biotin (Cys, +414.2 Da), carbamidomethyl (Cys), and oxidation (Met) were specified as variable modifications. Peptides were considered to be identified if their Mascot individual ion score was higher than the Mascot identity score (p < 0.05, ion score cutoff is 30). To evaluate the false discovery rate in protein identification, a decoy database search against a randomized decoy database created by Mascot using identical search parameters, and validation criteria was also performed. The search results obtained in Mascot were exported in eXtensive Markup Language (XML) data format. The MS/MS spectra and assignments for the identification of biotinylated peptides are presented in Supporting Information Figure S1. The mass spectrometry proteomics data have been deposited at the ProteomeXchange Consortium (http://proteomecentral. proteomexchange.org) via the PRIDE partner repository40 with the data set identifier PXD000467.

Protein Annotation and Motif Analysis

For subcellular localization, molecular function annotations, and information linked to diseases, all identified proteins were analyzed using the Ingenuity Pathway Analysis (IPA, http:// www.ingenuity.com) Knowledge Base and the Gene Ontology (GO) consortium. The potential protein−protein interactions and networks of the quantified S-nitrosylated proteins were further annotated by literature mining and IPA analysis. The potential consensus motifs of these identified endogenous Snitrosylated peptides were analyzed by the web-based tool SNOsite (http://csb.cse.yzu.edu.tw/SNOSite/)41 and presented using MDD Logo.42 The secondary structures of the identified proteins were analyzed by NetSurfP software (http:// genome.cbs.dtu.dk/services/NetSurfP/). Western Blot Assay

To detect the overall S-nitrosylation pattern, the total lysate was subjected to the S-alkylating biotin switch method, and 2 μg of biotinylated proteins was separated by 10% SDS-PAGE. To detect the protein S-nitrosylation level, 250 μg of biotinylated lysate was purified using 50 μL of streptavidin agarose and eluted in 30% ACN and 0.4% TFA. The eluted proteins were dried and resuspended in sample buffer. For protein detection, 20 μg of biotinylated lysate was separated by 10 or 12.5% SDS-PAGE and transferred to a poly(vinylidene fluoride) (PVDF) membrane (Millipore, Billerica, MA). The membrane was first incubated in TTBS [20 mM Tris-HCl at pH 7.4, 137 mM NaCl, 2.6 mM KCl, and 0.05% (v/v) Tween20] containing 5% nonfat milk at room temperature for 1 h with gentle agitation. The membrane was then incubated with the appropriate primary antibody: mouse anti-biotin monoclonal antibody conjugated with horseradish peroxidase (Sigma-Aldrich) at a 1:5000 (v/v) dilution, mouse anti-tubulin polyclonal antibody (Santa Cruz Biotechnology, Santa Cruz, CA) at a 1:2000 (v/v) dilution, goat anti-actin polyclonal antibody (Santa Cruz) at a 1:2000 (v/v) dilution, mouse antiGAPDH monoclonal antibody (Santa Cruz) at a 1:5000 (v/v) dilution, rabbit anti-iNOS polyclonal antibody (Santa Cruz) at a 1:500 (v/v) dilution, rabbit anti-thioredoxin polyclonal antibody (Abcam, Cambridge, MA) at a 1:5000 (v/v) dilution, rabbit anti-peroxiredoxin-4 polyclonal antibody (Abcam) at a 1:5000 (v/v) dilution, or rabbit anti-Annexin A4 polyclonal antibody (Abcam) at a 1:1000 (v/v) dilution at room temperature for 1 h with gentle agitation, followed by the peroxidase-conjugated secondary antibody at room temperature for 1 h. The membrane was washed three times with TTBS with gentle shaking at room temperature for 5 min, and the immunoreactive bands were visualized by an enhanced chemiluminescence detection system (Millipore).

Label-Free Quantitation

For label-free quantification, data analysis was performed using IDEAL-Q software.38 The raw data files from Waters Q-TOF Premier were converted to mzXML format by massWolf v4.0. After the Mascot database search, the confidently identified peptides (p < 0.05) from each LC−MS/MS run were exported in eXtensive Markup Language (XML) data format. IDEAL-Q was used to quantitatively analyze the xml files and the corresponding mzXML files. IDEAL-Q performed the ID-based elution time prediction using a fragmental regression (IDEAL) algorithm to predict the retention time of the peptide in the current run. On the basis of the retention times of the identified peptides, unidentified peptides can be detected and aligned by performing peak cluster detection near the peptide m/z (3), correct charge state, and correct isotope pattern. To further achieve confident identification of biotinylated peptides, a marker ion (m/z 270.1) that is characteristic of the fragmentation of biotin during MS/MS was included in the identification criteria. For the quantitation of each S-nitrosylated peptide, the abundance of S-nitrosylated peptides was normalized to the XIC peak area of the internal S-nitrosylated standard peptide (IS) in the same LC−MS/MS run (Figure 1C). The fold change in each peptide was calculated based on the normalized peptide abundance in different samples. Several differentially expressed S-nitrosylated proteins were further validated by western blot analysis. To evaluate the quantitation performance, we first chose two standard S-nitrosylated proteins, SNO-BSA and SNO-OVA, as internal standards. The in vitro S-nitrosylation of these two proteins was confirmed in our previous study.37 In addition, SNO-BSA and SNO-OVA are from different species (bovine and chicken) and thus the spiked S-nitrosylated peptide will not interfere with any homologous peptide in the human specimen. To establish the quantitation curve, approximately 800-fold of various concentrations (0.55−400 μg) of S-nitrosylated BSA and OVA were spiked into a human CC-M1 CRC cell lysate. As shown in Supporting Information Figure S2A, the working

Label-Free Strategy for Site-Specific Quantitation of the S-Nitrosoproteome

To quantify the S-nitrosylation level in an individual patient, in this study, we propose a simple multiplexed strategy that combines our S-alkylating biotin switch method with the labelfree strategy for the site-specific identification and quantitation of the S-nitrosoproteome. In brief, a standard S-nitrosylated protein, SNO-OVA, was spiked into the individual tumor or adjacent normal tissue as an internal standard to minimize the quantitation fluctuation, followed by the S-alkylating biotin switch, tryptic digestion, affinity purification, and LC−MS/MS analysis (Figure 1A). To improve the reproducibility and reliability of the label-free quantitation of individual samples, each batch of extracted peptides was analyzed by triplicate LC− MS/MS without a fractionation step. The quantitative computation of all S-nitrosylated peptides was performed by our previously reported IDEAL-Q software38 for peptide alignment and cross-assignment based on the information on the confidently identified peptide from the search results (Figure 1B). The computation algorithm adopts a nonlinear regression method for the identity-based alignment of all confidently identified S-nitrosylated peptides in the local elution time domain, followed by cross-assignment across multiple LC−MS/MS runs. Cross-assignment enables unidentified S-nitrosylated peptides, whether due to low identification scores or an unselected precursor for MS/MS sequencing, to be rescued by the predicted elution time and m/ 4946

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Figure 2. Western blot analysis of the expression patterns of iNOS and S-nitrosylated proteins. (A) Protein level of iNOS and GAPDH in the tumorous and adjacent normal tissues of 11 CRC patients detected by western blot; signals are shown in the bar chart. (B) iNOS protein abundance in the tumor and adjacent normal tissues of 19 CRC patients calculated and statistically analyzed by Student’s t-test for two groups. The red line indicates the mean ratio. (C) Total S-nitrosylation signals from tumor and adjacent normal CRC tissues detected by anti-biotin followed by the Salkylating biotin switch method with or without ascorbate addition. Five micrograms of total protein was separated by 10% SDS-PAGE, and labeled proteins were detected by western blot using anti-biotin. The iNOS protein in corresponding patient tissues was also detected. Actin and GAPDH protein served as the loading control.

curve for the SNO-BSA peptide (GLVLIAFSQYLQQC34PFDEHVK) normalized to the SNO-OVA peptide (ADHPFLFC367IK) reveals good linearity, with a coefficient of determination (R2) of 0.985. Similarly, the working curve of the Snitrosylated OVA peptide also reveals good linearity (R2 = 0.990, Supporting Information Figure S2B). The amount of these two S-nitrosylated proteins determined from the linear working curves was in good agreement with the known values (Supporting Information Figure S2C). We further evaluated the quantitation reproducibility on the S-nitrosoproteome scale in triplicate biological experiments, with the CC-M1 lysate spiked with BSA as an internal standard. As shown in Supporting Information Figure S2D, all three pairwise ratios showed narrow normal distributions with log2 mean values of 0.03 ± 0.80, −0.02 ± 0.77, and −0.06 ± 0.46 (95% confidence interval) from the 292 identified S-nitrosylated peptides. On the basis of the two-standard-deviation model (95% confidence), S-nitrosylated peptide ratios between 0.58 to 1.78 were considered unchanged levels. In the following experiments, we considered a difference of 2-fold to indicate a statistically significant degree of higher or lower expression.

normal tissue was 6.48. After excluding 3 patients who had none-to-all overexpression (designated as 99-fold in this study), the average ratio of iNOS expression normalized to GAPDH in the remaining 16 patients was 18.2 ± 34.0 fold higher in tumor tissues than in the adjacent normal tissues from individual patients (p < 0.005). On the basis of these results, we hypothesized that iNOS overexpression in tumor tissues might induce the change in the S-nitrosylation level of its target proteins. To detect the in vivo S-nitrosylation signals, these tissues were subjected to a biotin switch experiment followed by western blot analysis against biotin. Consistently, as shown in the representative examples in Figure 2C, the total Snitrosylation level was significantly higher in the cancerous tissues than in the adjacent normal tissues. For iNOS expression level, a similar trend for tumor and normal tissue was also observed for the 5 patients. Strong quantitative correlation of iNOS overexpression and S-nitrosylation in tumor tissues was observed for most patients (B2, C1, and C2), suggesting that overexpression of iNOS might induce the change in the S-nitrosylation level of its target proteins. It is noted that the false positive identification was evaluated by a negative control experiment without addition of ascorbate: only very weak signals were observed in both cancer and normal tissues. However, no S-nitrosylated proteins were detected from this experiment under our current detection sensitivity, revealing the specificity of S-nitrosylated protein identification in our approach.

Upregulation of iNOS Expression and S-Nitrosylation Levels in CRC Patients

To evaluate endogenous NO levels in CRC patients, we first analyzed the expression level of iNOS in paired tumor and adjacent normal tissues from CRC patients. As shown in the representative examples in Figure 2A, the western blot analysis revealed that iNOS was significantly overexpressed in CRC patients. The normalized abundance of iNOS/GAPDH from 19 CRC patients showed the average abundance of iNOS of 0.08 ± 0.17 and 0.53 ± 0.63 in adjacent normal and tumorous tissues (Figure 2B), respectively. The average ratio (T/N) of the relative intensity of iNOS in tumor to that of adjacent

Increasing the Number of Quantifiable S-Nitrosylated Proteins in CRC Tissues Using a Peptide Alignment and Cross-Assignment Strategy

To further identify the deregulated S-nitrosylated protein targets in vivo, we applied the label-free quantitation strategy to quantitatively compare the S-nitrosoproteome of the paired 4947

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Figure 3. Increasing number and decreasing tissue heterogeneity of S-nitrosylated proteins and peptides by label-free strategy. (A) The number of Snitrosylated proteins identified in each patient by LC−MS/MS analysis is indicated by gray bars. The peptide cross-assignment strategy increased the accumulated number of quantifiable S-nitrosylated proteins from the 11 CRC patients as shown by the white bars. (B) Quantifiable and accumulated S-nitrosylated peptides. (C) Protein localization, (D) function category, and (E) human diseases were classified from the 94 total S-nitrosylated proteins analyzed by IPA.

Localization, Functional Category, and Disease Annotation of in Vivo S-Nitrosylated Targets

tumor and corresponding adjacent normal tissues from 11 patients with CRC. The clinical information on the 11 patients is detailed in Supporting Information Table S1. Using conventional data-dependent triplicate LC−MS/MS analysis and database searches, as shown in Figure 3A,B (gray area), only 36−90 peptides corresponding to 21−39 S-nitrosylated proteins were identified in individual patients. Because patient heterogeneity results in diverse protein expression profiles, the identification results have low overlapping percentages among the 11 patients (Supporting Information Figure S3). Using the peptide alignment and cross-assignment strategy using the IDEAL-Q algorithm, every identified peptide can be informatically detected in all LC−MS/MS data sets from different patients. The number of identified S-nitrosylated peptides in individual patients was effectively increased from 36 to 90 peptides to a total of 199 unique S-nitrosylated peptides (Figure 3B). At the protein level, this result corresponds to an increase from a maximum of 21 proteins per patient to a total of 94 accumulated proteins (Figure 3A, false discovery rate ≤1%, see detailed identification information in Supporting Information Figure S1 and Table S2). This result indicates that the peptide alignment and cross-assignment strategy can utilize patient heterogeneity to effectively enhance the identification and quantification of S-nitrosylated targets in vivo. To verify the identification confidence of these low-abundance S-nitrosylated peptides that were initially identified in only one or few CRC patients, the presence of a marker ion (m/z 270.1) characteristic of biotin fragmentation during MS/MS was also confirmed in each spectrum (Supporting Information Figure S1).

To explore the potential functional roles of these 94 Snitrosylated proteins in CRC, annotations of subcellular localization and function were performed using the Ingenuity Pathway Analysis Knowledge Base (IPA) and the Gene Ontology (GO) consortium. As shown in Figure 3C, 41% of the identified S-nitrosylated proteins were located in the cytoplasm, 10% in the mitochondria, and 1% in the endoplasmic reticulum (ER). Interestingly, as many as 23% of the S-nitrosylated proteins were annotated in the extracellular space, and 19% of the S-nitrosylated proteins were associated with the membrane fraction, including the plasma membrane (13%), ER membrane (3%), mitochondrial membrane (2%), and Golgi apparatus membrane (1%). The functional categorization of these S-nitrosylated proteins revealed that the largest category is enzyme (25%); many of the Snitrosylated proteins are involved in oxidative response and metabolism (Figure 3D). In addition, some S-nitrosylated proteins were categorized as assembly and development components (13%), transporters (9%), peptidases (6%), and immune components (5%). The correlation of these 94 S-nitrosylated proteins with disease-related processes and networks was also analyzed by IPA (Supporting Information Table S2). As shown in Figure 3E, 61% of the S-nitrosylated proteins were matched to cancers; in particular, as many as 49% of these proteins have been reported to be associated with CRC. In addition to cancer, many proteins were categorized in inflammatory disease (53%), 4948

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Figure 4. MS-based quantitation heatmap and ratio validation of potential S-nitrosylated targets in human CRC. (A) The fold change was calculated by the XIC area of S-nitrosylated peptides in individual tumor tissues compared to the XIC area in the adjacent normal tissue. Each S-nitrosylated peptide was first normalized by the XIC area of the internal S-nitrosylated peptide in the same LC−MS/MS run. The heatmap revealed upregulation of the peptide ratio by ≥2-fold in more than 6 of 11 CRC patients. (B) S-Nitrosylated proteins were modified by the S-alkylating biotin switch method and purified by streptavidin. The purified S-nitrosylated/biotinylated proteins and total lysate of the tumor and adjacent normal tissues from the same patients were separated, and primary TXN, PRDX4, and ANXA4 antibodies were used to detect the expression level of the corresponding proteins by western blot. (C) The individualized ratios of the SNO-TXN/TXN protein (n = 21), SNO-PRDX4/PRDX4 protein (n = 24), and SNOANXA4/ANXA4 protein (n = 22) were compared between the tumor and adjacent normal tissues from the same patients. The red line indicates the mean ratio.

Cys199).32,46 For example, an increased level of S-nitrosylation at Cys237 of HSPD1 was observed in 9 patients. The HSPD1 protein has been reported to be overexpressed in CRC tissues.47,48 In addition, Cys237 has been reported to be a critical residue for mitochondrial Hsp60 S-nitrosylation to regulate mitochondrial biogenesis and intramitochondrial trafficking.43 Two other S-nitrosylation sites, Cys442 (≥7 patients) and Cys447 (≥9 patients), were also frequently upregulated. However, the potential involvement of these Snitrosylation sites in cancer remains to be established. Most importantly, many of the novel identified sites (14 of the top 19 sites) with high frequencies of elevated levels in patients have not been reported in vivo. For example, in this study, the S-nitrosylation site on Cys108 of ANXA4 was identified in vivo for the first time and showed elevated Snitrosylation levels in 7 patients (Figure 4A). In CRC, the high expression of ANXA4 in patients has been reported to be associated with a low survival rate and might serve as a potential biomarker for tumor diagnosis.49 Whether S-nitrosylation of ANXA4 is involved in cancer remains to be determined. Tissue redox activity has been reported as a hallmark of carcinogenesis.50 The upregulated S-nitrosylation of two redox-related proteins, peroxiredoxin-4 (PRDX4, Cys148) and neutrophil elastase (ELANE, Cys151), was observed in more than 6 patients. Other newly identified S-nitrosylated proteins in this

immunological disease (48%), and gastrointestinal disease (47%). Taken together, the results indicated that these Snitrosylated proteins show annotated roles in the regulation of the immune response, inflammation, and gastrointestinal diseases, particularly CRC. Whether the S-nitrosylation of these proteins plays any role in the initiation or progression of CRC remains to be elucidated. Quantitation Analysis of the Individualized S-Nitrosoproteome of CRC Patients

Using the criterion of a 2-fold change, 19 and 88 nitrosylated peptides were found to show a high occurrence of upregulation or downregulation (≥6 patients), respectively, in the tumorous tissues compared to paired adjacent normal tissues. The detailed quantitation results are presented in Supporting Information Table S2. On the basis of this individualized Snitrosoproteomic analysis, the heatmap indicates the different fold changes observed between the paired tumor and adjacent normal tissues in individual patients (Figure 4A). For the top 19 upregulated S-nitrosylated peptides, all 17 proteins were related to cancer development and tumorigenesis, as indicated by IPA. The results include some proteins known to be Snitrosylated at specific sites, such as the mitochondrial 60 kDa heat shock protein (HSPD1, Cys237),43 pyruvate kinase M2 (PKM2, Cys326),44 thioredoxin (TXN, Cys73),45 tubulin (TUBA1B, Cys347),31 and lactoylglutathione lyase (GLO1, 4949

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study, including LTF, DEFA1, RPS5, S100A11, ELANE, SNRPD2, and PRS28, were observed in CRC tissues for the first time (Figure 4A). Among these proteins, PRS28 has not been previously observed in any gastrointestinal system. Thus, this study not only revealed the endogenous S-nitrosylation profile of many cancer-associated proteins but also identified novel S-nitrosylation protein/sites in cancerous tissues.

tumor tissues, normalized based on the averaged ratio in all 21 adjacent normal tissues, was calculated to be 1.91 (Supporting Information Table S3 and Supporting Information Figure S4), revealing that such a calculation based on the average ratio ignores tissue and patient heterogeneity and does not reflect upregulated S-nitrosylation levels. In summary, the S-nitrosylation signals on TXN were upregulated and were more dramatic than its protein level in most of tumor tissues of individual CRC patients. The results highlight the importance of determining the individualized S-nitrosylation ratio to identify the altered S-nitrosylated protein level in CRC patients. Similar results regarding the elevated individualized ratio were observed for ANXA4 and PRDX4. An upregulated level of ANXA4 S-nitrosylation was observed in 7 patients. The average fold change in the individualized S-nitrosylation level of ANXA4 was 19.79 ± 36.28. After normalization to the change in the protein expression level, the individualized SNOANXA4/ANXA4 ratio, which corresponded to the differential S-nitrosylation degree of ANXA4, was found to be 10.93 ± 23.18 fold (Supporting Information Table S3 and Supporting Information Figure S5). Moreover, S-nitrosylation signal on ANXA4 may be modulated by its protein level due to higher expression level of ANXA4 protein in tumor samples. PRDX4 exhibited a comparatively less dramatic change and a lower frequency of upregulation. Only 5 and 6 patients showed upregulation at the protein expression and S-nitrosylation levels, respectively. After normalization to the change the individualized protein ratios, the change in the extent of Snitrosylation, i.e., the SNO-PRDX4/PRDX4 ratio, was calculated as 2.32 ± 4.16 (Supporting Information Table S3 and Supporting Information Figure S6). Taken together, these results demonstrate that the detected S-nitrosylation alteration may be regulated by both the expression level and the posttranslational S-nitrosylation of target proteins.

Validation of S-Nitrosylation and Protein Expression Level by Western Blot

On the basis of the detailed information on the reported expression in cell lines, tissues, and serum by IPA analysis (Table 1), all of the top 17 S-nitrosylated proteins with a high occurrence (≥6 patients) of overproduction of S-nitrosylation from four stages (Figure 4B) were expressed in either CRC tissues or the large intestine and related to cancer. To further validate the differential S-nitrosylated proteomic profiles in the tissues obtained from our label-free strategy, three S-nitrosylated proteins, TXN, PRDX4, and ANXA4, were chosen based on the following three criteria: (1) potential involvement in CRC, (2) confirmed expression in the large intestine and epidermis cells, and (3) the availability of commercial antibodies. These three candidates were further examined to differentiate whether the overproduced S-nitrosylation arose from the protein expression or the S-nitrosylation level. The increased expression of TXN has been linked with decreased survival in CRC patients.51 As shown in the representative results for patients at different stages (Figure 4B and Supporting Information Figure S4 for complete data), significantly increased S-nitrosylation of TXN was observed for most patients (13 out of 21 patients), while the protein expression of TXN was also slightly increased (6 out of 21 patients). It should be noted that while increased S-nitrosylation at Cys73 was identified by our approach, the western blot results revealed only the total S-nitrosylation level of TXN. On the basis of the western blot analysis of TXN, the Snitrosylated TXN signal in tumor tissues was significantly increased over the adjacent normal tissues (n = 21, p < 0.005, Supporting Information Figure S4). Thus, we further calculated the individualized ratios between the paired tumor and adjacent normal tissues from the same patients. As shown in Figure 4C and Supporting Information Table S3, 13 of 21 patients exhibited ≥2-fold upregulated S-nitrosylation of TXN, including 3 patients with dramatic none-to-all overexpression. The average fold-change in the individualized level of the Snitrosylation of TXN normalized based on GAPDH protein expression was 18.26 ± 34.61 fold (Supporting Information Table S3). Of the 13 patients, only 6 patients displayed upregulation at the protein expression level (average ratio: 1.96 ± 2.05) (Supporting Information Figure S4), indicating that the alteration of S-nitrosylation is more dramatic than that of the protein expression level. To further reveal the extent of elevated S-nitrosylation in the tumors, the average fold change in the individualized S-nitrosylation level of TXN normalized by the individual protein expression level (S-nitrosylated TXN/ TXN) was calculated to be 17.61 ± 34.74 fold (Figure 4C and Supporting Information Table S3). The high standard deviation reflects the different characteristics or tissue heterogeneity among the CRC patients. As a comparison between the individualized ratio (i.e., the tumor/normal ratio of an individual patient) and the averaged ratio conventionally obtained by pooling samples from different patients, the averaged ratio of the S-nitrosylated TXN signals in the 21

Consensus Motifs and Structural Properties of the S-Nitrosylated Cysteines

In contrast to the many well-studied phosphorylation motifs of kinases and phosphatases, the structural or sequence determinants for protein S-nitrosylation are poorly defined. To further elucidate and characterize the potential linear consensus motifs and adjacent amino acids surrounding the endogenous Snitrosylated cysteine residues, we input these 174 S-nitrosylation sites and 1340 free cysteine residues from the 94 identified S-nitrosylated proteins to compare the positionspecific differences in amino acid composition in the 21-mer window (−10 ∼ +10) surrounding the S-nitrosylation and nonS-nitrosylation sites (free cysteines). As shown by the enriched amino acids (upper panel of Figure 5A), the most pronounced feature of S-nitrosylation sites is the compositional biases toward hydrophobic amino acids, particularly V/L/A at positions −2, +1 ∼ +5, and +8; toward positively charged amino acids R/K/H at positions −9, −7, −3, +2, +4, +5, and +10; and toward negatively charged amino acids D/E at positions +6 and +10. Another featured characteristic is the depletion of amino acids, particularly cysteine (C), surrounding the central free cysteine between positions −6 and +10 in the non-S-nitrosylation data set (Figure 5A). These results reveal that the distant amino acids in the sequence are notably different between S-nitrosylation sites and non-S-nitrosylation sites. To understand the structural distribution of in vivo Snitrosylation, the secondary structures of the 174 S-nitro4950

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4951

GLO1

S100A11

TUBA1B

ELANE

SNRPD2

DEFA1

Q04760

P31949

P68363

P08246

P62316

P59665

EEF2

LTF

P02788

P13639

HSPD1

P10809

PRDX4 RPS5

ANXA4

P09525

Q13162 P46782

YWHAZ

P63104

LYZ

PKM2

P14618

P61626

TXN

gene

P10599

UniProt no.

Small nuclear ribonucleoprotein Sm D2 Neutrophil defensin 1

Tubulin alpha-1B chain Neutrophil elastase

Lactoylglutathione lyase Protein S100-A11

Peroxiredoxin-4 40S ribosomal protein S5 Elongation factor 2

Lysozyme C

60 kDa heat shock protein, mitochondrial Lactotransferrin

Annexin A4

14-3-3 protein zeta/ delta

Pyruvate kinase isozymes M1/M2

Thioredoxin

protein name

×

× ×

− − − ×

− −

− × × ×

− −

−

×

−

×

−

×

− −

−

×

− −

×

×

×

×

bloodb

×

plasma/ serumb

−

−

×

−

−

×

−

×

×

−

× ×

×

−

×

×

×

×

×

×

×

×

−

× ×

×

−

−

×

×

×

×

Mϕb

monocytederived Mϕb

−

−

−

−

×

−

−

− ×

×

×

−

×

×

−

×

neutrophilsb

−

−

−

−

−

×

×

× ×

−

×

×

×

×

×

×

epidermisb

−

−

−

−

−

×

×

× ×

−

×

×

×

×

×

×

large intestineb

×

−

−

×

−

−

×

× −

×

×

×

×

×

×

×

expressedc in CRC tissues

Ex

N

Ex

C

C

C

C

C C

Ex

Ex

M

PM

C

C

C

locationd

Antimicrobial Response, Cancer

Antimicrobial Response, Cancer, Cardiovascular Disease, Connective Tissue Disorders, Developmental Disorder, Endocrine System Disorders, Gastrointestinal Disease, Genetic Disorder, Hematological Disease, Immunological Disease, Infectious Disease, Inflammatory Disease, Metabolic Disease, Oganismal njury and Abnormalities Cancer

Cancer, Genetic Disorder, Immunological Disease, Inflammatory Disease Cancer, Genetic Disorder

Cancer, Connective Tissue Disorders, Endocrine System Disorders, Gastrointestinal Disease, Genetic Disorder, Immunological Disease, Inflammatory Disease, Metabolic Disease Cancer, Genetic Disorder

Cancer, Connective Tissue Disorders, Endocrine System Disorders, Gastrointestinal Disease, Genetic Disorder, Hematological Disease, Hypersensitivity Response, mmunological Disease, nfectious Disease, Inflammatory Disease, nflammatory Response, Metabolic Disease Cancer, Dermatological Diseases and Conditions, Gastrointestinal Disease, Genetic Disorder, Neurological Disease, Renal and Urological Disease, Skeletal and Muscular Disorders Cancer, Gastrointestinal Disease, Genetic Disorder, Hematological Disease, Neurological Disease, Reproductive System Disease, Skeletal and Muscular Disorders Antimicrobial Response, Cancer, Connective Tissue Disorders, Endocrine System Disorders, Gastrointestinal Disease, Genetic Disorder, Hematological Disease, Immunological Disease, nfectious Disease, Inflammatory Disease, nflammatory Response, Metabolic Disease, Cancer, Connective Tissue Disorders, Genetic Disorder, Immunological Disease, Infectious Disease, Inflammatory Disease, Inflammatory Response Cancer, Connective Tissue Disorders, Endocrine System Disorders, Gastrointestinal Disease, Genetic Disorder, Hematological Disease, Hypersensitivity Response, Immunological Disease, Infectious Disease, Inflammatory Disease, Inflammatory Response, Metabolic Disease Antimicrobial Response, Cancer, Connective Tissue Disorders, Genetic Disorder, Immunological Disease, Inflammatory Disease, Inflammatory Response, Metabolic Disease Cancer, Genetic Disorder Cancer, Infectious Disease

diseased

Table 1. Top 17 S-Nitrosylated Proteins with 2-Fold Higher Expression in More Than 6 of 11 CRC Patients and Detailed Information on These Proteinsa

Cys-83

Cys-63

Cys-151

37

Cys-13

44, 65

Cys-290

Cys-148 Cys-172

Cys-48

Cys-608

Cys-442

Cys-108

Cys-94

63

64

novel site

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Article

sylation sites and a total of 1514 cysteine residues among the 94 proteins identified in this study were also analyzed (Figure 5B). Among the three types of secondary structure, 22.2% cysteines on α-helices were S-nitrosylated, representing the highest frequency of S-nitrosylation, followed by the less abundant βstrand (16.6%) and coil (6.3%). Compared to the distribution of free cysteine, the in vivo S-nitrosoproteomic profiles revealed a higher frequency of S-nitrosylation on α-helices. This result is in contrast to the in vitro S-nitrosoproteomic data set, which revealed a higher frequency of S-nitrosylation on β-strands in SNAP/L-cysteine-stimulated endothelial cells.37 Among the potential consensus S-nitrosylation sequences, 71 and 82 peptides were, respectively, categorized into hydrophobic L/ AxC and CxV/L/A motifs (p < 0.005) (Figure 5C), including the well-known S-nitrosylation sites Cys81 of MIF, Cys48 of GSTP1, Cys376 of TUBA1B, Cys152 of GAPDH, and Cys94 of HBB/HBD. The distribution of the secondary structure of Snitrosocysteine was consistent with these surrounding hydrophobic motifs, which are preferentially located on β-strands (Figure 5C). In addition, 36 and 41 peptides were, respectively, matched to the positively charged amino acids R/K at positions −7 and −9 (Figure 5D), such as Cys326 of PKM2 and Cys148 of PRDX4, and 32 and 28 peptides were matched to the negatively charged amino acids D/E at positions +6 and +10 (Figure 5E) with a stringent statistical threshold (p < 0.005). In contrast to the distribution of S-nitrosocysteines, the acidic and basic motifs surrounding S-nitrosocysteines were preferentially located on α-helix and coil secondary structures (Figure 5D,E). Taken together, our data suggest that acid/base and local hydrophobicity at the secondary structure level in the threedimensional protein environment may contribute to the specificity of reactive cysteine toward S-nitrosylation in vivo.

a Detailed information includes subcellular localization, associated diseases, novel S-nitrosylation site, and expression in potential cell types, tissues, and serum in the gastrointestinal system. bThe expression data are obtained from Ingenuity Pathway Analysis. cThe expression of proteins in colorectal cancer tissues are obtained in Human Protein Reference Database. dThe location and function are annotated by Gene Ontology and Ingenuity Pathway Analysis Knowledge Base. C, Cytoplasm; Ex, extracellular space; M, mitochondrion; N, nucleus; PM, plasma membrane.

Cys-27 C − − − − − − − − RPS28 P62857

40S ribosomal protein S28

bloodb gene UniProt no.

Table 1. continued

protein name

plasma/ serumb

Mϕb

monocytederived Mϕb

neutrophilsb

epidermisb

expressedc in CRC tissues large intestineb

locationd

Cancer

diseased

novel site

Journal of Proteome Research

Endogenous S-Nitrosoproteomic Network in CRC Patients

To explore the relationship and functional assignments in molecular signaling pathways of the 94 identified S-nitrosylated proteins in CRC patients, we further assembled the potential human S-nitrosoproteomic network through reported direct and indirect protein−protein interactions and the human diseases using Ingenuity Pathway Analysis (IPA). As shown in Figure 6, our S-nitrosoproteomic atlas in CRC tissue revealed many S-nitrosylated targets that have been annotated as relating to cancer or inflammation. Here, we presented four groups of proteins in the S-nitrosoproteomic atlas. First, HSPD1, ELANE, SOD1, FLNA, VIM, GSTP1 proteins were categorized as the downstream substrates of iNOS-mediated S-nitrosylation. The second group was associated with the transnitrosylase TXN. Many proteins containing upregulation of Snitrosylation level, including PRDX4, CAT, MIF, EEF2, SOD1, and YWHAZ, have been reported to directly interact with TXN by IPA analysis. Some proteins, such as PKM2, AK2, HSPD1, TUBA1B, and ECHS1, which interact indirectly with TXN, were commonly upregulated in ≥6 CRC patients. In these two groups, most of proteins were associated with inflammation and cancer. The third group of proteins was linked with cancer. Surprisingly, many plasma membrane proteins or associated proteins, including LTF, LYZ, ELANE, ANXA1, ANXA4, S100A11, RPS5, DEFA1, and GLO1, were observed to be Snitrosylated, whereas a high percentage of S-nitrosylated proteins were annotated in the extracellular region of secretion, including HBB/HBD, TF, MUC2, FGG/FGB, ALB, DCN, and HP, and were downregulated in ≥6 CRC patients, categorizing into the fourth group. Moreover, the majority of these proteins 4952

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Figure 5. Consensus motif and secondary structure distribution of endogenous S-nitrosylation in human CRC. (A) A total of 174 S-nitrosylated cysteines and 1340 non-nitrosylated cysteines from 94 identified proteins in 11 CRC patients were analyzed using the SNOsite Web site. The compositional biases of the amino acids around the S-nitrosylation sites (upper panel) were compared with those of the non-S-nitrosylation sites (lower panel), p < 0.005. (B) The frequency of S-nitrosylated cysteines in different secondary structures was calculated and is presented in the bar chart. (C) MDD logo identified 6 potential consensus motifs of endogenous S-nitrosylation cysteines with a stringent statistical threshold (p < 0.005). Three motif groups, including aliphatic, acid, and basic groups, surrounding the S-nitrosocysteines were further analyzed for their secondary structure distribution.

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DISCUSSION Despite increasing evidence linking NO to cancer,13 a systematic understanding of how S-nitrosylation contributes to tumorigenesis has been lacking. The majority of previous studies correlating abnormally regulated S-nitrosylation and disease have not been based on proteomic approaches.52 To delineate the pathological S-nitrosoproteome in cancer, in this study, we reported a label-free quantitative strategy for the individualized analysis of the degree of endogenous Snitrosylation in individual CRC patients. In contrast to previous studies using isotopic labeling,29,31−33 this label-free strategy can be applied to a unlimited number of multiplexed cell, animal, or clinical samples. The applied peptide alignment and cross-assignment strategy improved the analytical sensitivity through the informatic recovery of low-abundance peptides under fractionation-free conditions. The heterogeneity of the tissue profiles significantly improved the identification coverage of the CRC S-nitrosoproteome; as high as a 5-fold increase in the accumulative number of quantifiable S-nitrosylated peptides was achieved compared to the results from a single patient. Compared to the sample pooling strategy, this high-throughput strategy is advantageous for providing individualized S-nitrosoproteomics profiles. The quantitative comparison between the tumor and adjacent normal tissues from the same patients is important to minimize the effect of genetic variations and to

have been reported to participate in inflammation and the immune response. However, the S-nitrosylation modifications of these proteins have not been previously documented, such as Cys108 of ANXA4 and Cys608 of LTF. On the basis of the linking of human disease by IPA analysis, most of S-nitrosylated proteins, including TXN, EEF2, PKM2, HSPD1, AK2, ELANE, LTF, LYZ, ANXA1, and S100A11, having upregulation level of S-nitrosylation in tumor tissues compared to the adjacent normal tissues in CRC patients, are involved in both of inflammation and cancer (Figure 6). The result indicated that these S-nitrosylated proteins may regulate or be regulated in the formation of inflammation and tumor progression. Moreover, some upregulated S-nitrosylated proteins, such as PRDX4, ECHS1, YWHAZ, TUBA1B, RPS5, and ANXA4, were categorized as cancer-related proteins. Among them, the PRDX4 and ANXA4 were observed to have elevated protein expression level as well as increased Snitrosylation level (Figure 4). It suggested that these proteins may be translationally regulated in cancers, which further influences their S-nitrosylation level. However, the relationship between S-nitrosylation and cancer requires further clarification. 4953

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Figure 6. Proposed mechanotransduction networks for CRC and S-nitrosylation by subcellular localization and protein−protein interaction. For the IPA analysis, the S-nitrosylated targets are shown based on experimental evidence of direct or indirect protein−protein interaction and subcellular localization. The proteins involved in cancer, inflammation, and other responses are also presented.

crucial factors in regulating the signaling transduction of Snitrosylation.

overcome the intrinsic intra- and interspecimen variability associated with different patient characteristics and tissue heterogeneity.53 Because the NO species is short-lived in vivo, many studies have reported that specific protein−protein interactions contribute to the iNOS-induced S-nitrosylation of downstream targets43,54 and the specificity of TXN-mediated transnitrosylation.45 As shown in Figure 6, we determined that most of the identified S-nitrosylated proteins have documented annotations of protein−protein interaction and human disease relating to cancer or inflammation using Ingenuity Pathway Analysis (IPA).

Interaction with Transnitrosylase

The second group in the S-nitrosoproteomic atlas is associated with the transnitrosylase TXN. In addition to direct modification by NO or nitrosylating equivalents, transnitrosylation, which involves the direct thiol-to-thiol transfer of an NO group from a low-molecular-weight S-nitrosothiol or a SNO-protein to a target protein, is an important mechanism when the target cysteines do not appear to be candidates for modification based on their reactivity.56 TXN is one of the few well-studied regulators to catalyze the transnitrosylation and denitrosylation of specific targets, depending on the redox status of different cysteine residues.28,45 Abnormal expression of the well-known nitrosylase TXN has been implicated in inflammation and cancer,57,58 particularly in promoting the growth of tumors and regulating cancer development in the colon,51 lung,59 and liver.60 In this study, a high occurrence of upregulated S-nitrosylated TXN was observed, and many identified S-nitrosylated proteins have been annotated as being associated with TXN through direct or indirect interactions (Figure 6). In the role of trans- or denitrosylase, TXN might mediate reversible (trans)-nitrosylation and denitrosylation in vivo, thereby providing fine control to maintain the redox balance in cells. TXN may mediate the functionally divergent Snitrosylation on its substrates. Potential nitrosylation targets of TXN as a transnitrosylasehave been reported by Wu et al.,45 including chaperones, redox enzymes, structural proteins, and metabolic enzymes. Many of the identified TXN substrates displayed high occurrences of upregulated S-nitrosylation levels in CRC patients in this study, including Cys237 of HSPD1, Cys148 of PRDX4, Cys81 of MIF, Cys326 of PKM2, and Cys290 of EEF2 (Supporting Information Table S2), which suggests that

Interaction with iNOS

The first group of proteins in the S-nitrosoproteomic atlas, which included HSPD1, ELANE, and FLNA, was categorized as the downstream substrates of iNOS-mediated S-nitrosylation based on direct or indirect protein−protein interactions (Figure 6). One of the inducing factors involved in the formation and progression of CRC is the overexpression of iNOS, which results in the production of a high concentration of NO.55 The sustained induction of iNOS might arise from chronic inflammation and transform the adenoma at an early stage to carcinoma.22 In addition, the physiological interaction of iNOS with its substrate facilitates S-nitrosylation and downstream enzymatic activation.54 Among these differentially regulated substrates for S-nitrosylation, the upregulation of HSPD1 has been reported in CRC,47,48 and the nitrosylation site on Cys237 has been reported to be a critical residue in the interaction of Snitrosylated HSPD1 with iNOS to regulate inflammationinduced mitochondrial biogenesis and intramitochondrial trafficking.43 Our observation suggests that downstream substrates that directly interact with iNOS in vivo may be 4954

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be the first line of antioxidant defense.68 The highly conserved active site Cys47 of PRDX6 is located in a hydrophobic pocket, and its peroxidase activity may be regulated by oxidation,69,70 suggesting that S-nitrosylation also regulates its activity through Cys47.

trans-nitrosylation by S-nitrosylated TXN may account for the upregulated S-nitrosylation levels. By contrast, some substrates, such as YWHAZ and HSPD1, have been reported to be denitrosylation substrates of the TXN/TXN reductase system.32 We further explored whether this group of S-nitrosylated TXN-associated proteins might include reported motifs, such as AxC,45 which was also identified as a major motif in this study. Consistently, most of the identified S-nitrosylation sites on TXN-interacting proteins in this study possessed a consensus motif in which alanine is located near the S-nitrosocysteine. For example, the S-nitrosylation sites on SOD1, YWHAZ, GAPDH, GSTP1, and TUBA1B are located in the AxC motif; the Snitrosylation site of PRDX4 and YWHAZ is located in the AC motif; and the S-nitrosylation site of HSPD1 presents CxxxxA, CxxA, and CA motifs. Taken together, TXN-mediated transnitrosylation may play a major role in spatial and temporal modifications in cellular antioxidant systems.45

Iron-Carrier Proteins

Under conditions of redox imbalance, S-nitrosothiol and iron supplementation can regulate intestinal inflammation and the subsequent development of CRC.71 In our endogenous Snitrosylation data set, the fourth major group included some secreted iron carrier proteins, such as hemoglobin subunit β/δ (HBB/HBD), haptoglobin (HP), transferrin (TF), lactotransferrin (LTF), and fibrinogen β/γ chain (FGB/FGG) (Figure 6). During inflammation and oxidative stress, the peroxidase activity of the HB−HP complex functions to scavenge NO in plasma and to inhibit macrophages.72 NO equivalents are transported in the blood via HB by S-nitrosylation of Cys93.73 Defects in NO synthesis due to S-nitrosylated HB may contribute to hypoxemia.74 S-Nitrosylation of TF has been identified in plasma from hemorrhaged rats and confirmed by in vitro experiments using the biotin switch method.75 LTF, a high-affinity iron-binding glycoprotein produced by macrophages, can interact with NO generated from LPS-stimulated macrophages through the formation of an Fe−NO complex76 and can contribute to antimicrobial activity.77 Quantitation of the newly identified S-nitrosylation site on Cys608 of LTF revealed a high occurrence of upregulation in CRC patients (Figure 4); however, it is unclear if S-nitrosylation on Cys608 occurs via transnitrosylation from the Fe−NO complex. The observed S-nitrosylation of these S-nitrosylation carriers may suggest their potential roles in the regulation of CRC by governing the chemical reactivity and mechanism of transnitrosylation between cells and tissues through the circulatory system.

Cancer-Related Proteins

The third group of proteins shows established relationships with cancer. In addition to the involvement of the TXN system in cancer, many of the S-nitrosylated proteins identified in this study, such as LTF, ELANE, ANXA4, DEFA1, and GLO1, are upregulated in many cancers. However, the S-nitrosylation modifications of these proteins have not been previously documented. For example, high expression of ANXA4 in CRC patients has been reported to be associated with a low survival rate and might serve as a potential biomarker for tumor diagnosis.49 In this study, we identified a previously unknown novel S-nitrosylation site at Cys108 of ANXA4. The potential correlation of this site with functions and mechanisms involved in cancer remains to be further investigated. Cys139 of GLO1 was identified as showing a high occurrence of increased levels of S-nitrosylation in the tumorous tissues of CRC patients in our study. GLO1 has been reported to be upregulated in metastatic melanoma tissues and could regulate the detoxification of reactive methylglyoxal in the glycolytic pathway in breast and prostate cancer cells.61 The activity of the GLO1 protein can be inhibited by the natural antioxidant curcumin, which presents multifaceted functions in anti-inflammation and antitumor processes.61 Moreover, previous studies have shown that the activity of GLO1 can be inhibited by glutathionylation of Cys139 in vitro62 and that NO can inactivate and interact with GLO1.46,63 Taken together with the findings of the present study, these results indicate Cys139 of GLO1 as a potential novel substrate for S-nitrosylation. Some S-nitrosylated proteins in our study, such as ANXA2, CALR, and PRDX6, have also been identified in the colon adenocarcinoma cell line HCT-11627 and shown to regulate tumor progression. For example, NO has been reported to inhibit ANXA2 activity, leading to the induction of membrane fusion and inhibition of tumor migration in prostate cancer.64,65 Whether the identified endogenous S-nitrosylation sites on Cys133 and Cys262 of ANXA2 are correlated with this inhibition is unknown. CALR, a calcium-binding chaperone in the ER, has been suggested to have inhibitory effects on tumor growth and angiogenesis.66 Increased CALR production can cause NOS activation and NO production in lung endothelial cells.67 However, the potential role of the S-nitrosylation of CALR (Cys105) in the regulation of its function requires further investigation. PRDX6, an antioxidant enzyme, is highly expressed in the gastrointestinal epithelia, suggesting it may

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CONCLUSIONS Our quantitative S-nitrosoproteomic study suggested that Snitrosylation sources, whether iNOS, transnitrosylase, or iron− sulfur centers, and their targets in the protein S-nitrosylation signaling network may be involved in the progression of colon cancer. Upregulation of S-nitrosylation on TXN in tumor tissue of CRC patients suggests that it may regulate or be regulated in the formation of inflammation and tumor progression. Some cancer-related S-nitrosylated proteins, such as PRDX4 and ANXA4, may be regulated in cancer and further influence their S-nitrosylation level. However, the relationship between Snitrosylation and cancer still needs to be clarified. The subcellular localization and protein−protein interaction annotation of these identified S-nitrosylation targets may also suggest that the spatial distribution of reversible S-nitrosylationmediated redox mechanisms modulates the expression level and subcellular compartmentalization of S-nitrosylation signaling molecules in different patients. In spite of discovery of endogenous S-nitrosoproteome in clinical cancer tissues, the absolute quantitation of S-nitrosylation level on proteins needs to be developed in the future. Due to the small sample size and sample amount from tissue samples, which is the main limitation in our case for control study, the detection limitation of the biotin switch method is a challenge. Given the demonstrated sensitivity of individualized tissue analysis, we expect that the label-free approach may facilitate the study of the vastly under-represented S-nitro4955

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regulates nitric oxide-mediated cell invasion. J. Biol. Chem. 2010, 285, 3806−3814. (3) Li, F.; Sonveaux, P.; Rabbani, Z. N.; Liu, S.; Yan, B.; Huang, Q.; Vujaskovic, Z.; Dewhirst, M. W.; Li, C.-Y. Regulation of HIF-1α stability through S-nitrosylation. Mol. Cell 2007, 26, 63−74. (4) Tada, Y.; Spoel, S. H.; Pajerowska-Mukhtar, K.; Mou, Z.; Song, J.; Wang, C.; Zuo, J.; Dong, X. Plant immunity requires conformational charges of NPR1 via S-nitrosylation and thioredoxins. Science 2008, 321, 952−956. (5) Lima, B.; Forrester, M. T.; Hess, D. T.; Stamler, J. S. SNitrosylation in cardiovascular signaling. Circ. Res. 2010, 106, 633− 646. (6) Gu, Z.; Kaul, M.; Yan, B.; Kridel, S. J.; Cui, J.; Strongin, A.; Smith, J. W.; Liddington, R. C.; Lipton, S. A. S-Nitrosylation of matrix metalloproteinases: signaling pathway to neuronal cell death. Science 2002, 297, 1186−1190. (7) Uehara, T.; Nakamura, T.; Yao, D.; Shi, Z.-Q.; Gu, Z.; Ma, Y.; Masliah, E.; Nomura, Y.; Lipton, S. A. S-Nitrosylated proteindisulphide isomerase links protein misfolding to neurodegeneration. Nature 2006, 441, 513−517. (8) Bogdan, C. Nitric oxide and the immune response. Nat. Immunol. 2001, 2, 907−916. (9) Clancy, R. M.; Amin, A. R.; Abramson, S. B. The role of nitric oxide in inflammation and immunity. Arthritis Rheum. 1998, 41, 1141−1151. (10) Xu, W.; Liu, L. Z.; Loizidou, M.; Ahmed, M.; Charles, I. G. The role of nitric oxide in cancer. Cell Res. 2002, 12, 311−320. (11) Erdman, S. E.; Rao, V. P.; Poutahidis, T.; Rogers, A. B.; Taylor, C. L.; Jackson, E. A.; Ge, Z.; Lee, C. W.; Schauer, D. B.; Wogan, G. N.; Tannenbaum, S. R.; Fox, J. G. Nitric oxide and TNF-α trigger colonic inflammation and carcinogenesis in Helicobacter hepaticus-infected, Rag2-deficient mice. Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 1027− 1032. (12) Jaiswal, M.; LaRusso, N. F.; Gores, G. J. Nitric oxide in gastrointestinal epithelial cell carcinogenesis: linking inflammation to oncogenesis. Am. J. Physiol.: Gastrointest. Liver Physiol. 2001, 281, G626−G634. (13) Fukumura, D.; Kashiwagi, S.; Jain, R. K. The role of nitric oxide in tumour progression. Nat. Rev. Cancer 2006, 6, 521−534. (14) Wang, Z. Protein S-nitrosylation and cancer. Cancer Lett. 2012, 320, 123−129. (15) Aranda, E.; Lopez-Pedrera, C.; Haba-Rodriguez, J. R. D. L.; Rodriguez-Ariza, A. Nitric oxide and cancer: the emerging role of Snitrosylation. Curr. Mol. Med. 2012, 12, 50−67. (16) Burke, A. J.; Sullivan, F. J.; Giles, F. J.; Glynn, S. A. The yin and yang of nitric oxide in cancer progression. Carcinogenesis 2013, 34, 503−512. (17) Sappayatosok, K.; Maneerat, Y.; Swasdison, S.; Viriyavejakul, P.; Kittipong; Dhanuthai; Zwang, J.; Chaisri, U. Expression of proinflammatory protein, iNOS, VEGF and COX-2 in oral squamous cell carcinoma (OSCC), relationship with angiogenesis and their clinicopathological correlation. Med. Oral Patol. Oral Cir. Bucal. 2009, 14, E319−E324. (18) Farrow, B.; Evers, B. M. Inflammation and the development of pancreatic cancer. Surg. Oncol. 2002, 10, 153−169. (19) Slomiany, B. L.; Slomiany, A. Role of constitutive nitric oxide synthase in regulation of Helicobacter pylori-induced gastric mucosal cyclooxygenase-2 activation through S-nitrosylation: mechanism of ghrelin action. Open J. Gastroenterol. 2011, 1, 13−22. (20) Cianchi, F.; Cortesini, C.; Fantappiè, O.; Messerini, L.; Sardi, I.; Lasagna, N.; Perna, F.; Fabbroni, V.; Di Felice, A.; Perigli, G.; Mazzanti, R.; Masini, E. Cyclooxygenase-2 activation mediates the proangiogenic effect of nitric oxide in colorectal cancer. Clin. Cancer Res. 2004, 10, 2694−2704. (21) Ambs, S.; Merriam, W. G.; Bennett, W. P.; Felley-Bosco, E.; Ogunfusika, M. O.; Oser, S. M.; Klein, S.; Shields, P. G.; Billiar, T. R.; Harris, C. C. Frequent nitric oxide synthase-2 expression in human colon adenomas: implication for tumor angiogenesis and colon cancer progression. Cancer Res. 1998, 58, 334−341.

soproteome and enable the elucidation of the endogenous Snitrosylation effect at the molecular level in cancer.

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ASSOCIATED CONTENT

S Supporting Information *

Table S1: Clinicopathologic information on CRC patients. Table S2: Summary of all 94 S-nitrosylated proteins from 11 CRC patients. Table S3: Individualized fold-change of Snitrosylation and protein levels for TXN, ANXA4, and PRDX4 in CRC patients by western bot. Figure S1: MS/MS spectra and assignment for S-nitrosylated/biotinylated peptides of identified proteins in 11 human colorectal cancer (CRC) patients based on signal unique peptide spectra. Figure S2: Evaluation of quantitation linearity, dynamic range, accuracy, and reproducibility by S-nitrosylated standard proteins. Figure S3: The distribution of identified S-nitrosylated proteins in 11 CRC patients. Figure S4. Validation of S-nitrosylation and protein level of TXN from 21 CRC patients by western blot. Figure S5: Validation of S-nitrosylation and protein level of ANXA4 from 22 CRC patients by western blot. Figure S6: Validation of S-nitrosylation and protein level of PRDX4 from 24 CRC patients by western blot. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Tel: +886-2-2789-8660. Fax: +886-2-2783-1237. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the National Science Council in Taiwan (NSC-100-2627-M-001-003 and NSC-100-2922-I-016002). Deposition of the data in the ProteomeXchange Consortium was supported by the PRIDE Team, EBI.

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ABBREVIATIONS NO, nitric oxide; iNOS, inducible nitric oxide synthase; CRC, colorectal cancer; SNOSID, S-nitrosothiol site identification; SNO-RAC, S-nitrosothiol resin-assisted capture; biotin-HPDP, N-[6-(biotinamido)hexyl]-3′-(2′-pyridyldithio)-propionamide; SNOCAP, S-nitrosothiol capture; 2D-DIGE, two-dimensional fluorescence difference gel electrophoresis; cICAT, acidcleavage isotopic-code affinity tag; NEM, N-ethylmaleimide; cysTMT6, cysteine-reactive tandem mass tag; iTRAQ, isobaric tags for relative and absolute quantification; SILAC, stable isotope labeling by amino acids in cell culture; LPS, lipopolysaccharide; IFNγ, interferon gamma; PTM, posttranslational modification; SNAP, S-nitroso-N-acetylpenicillamine; BSA, bovine serum albumin; OVA, ovalbumin; Cys, cysteine; IDEAL-Q, ID-based elution time prediction by fragmental regression; XIC, extracted ion chromatogram; TXN, thioredoxin; PRDX4, peroxiredoxin-4; ANXA4, annexin A4

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REFERENCES

(1) Mannick, J. B.; Schonhoff, C.; Papeta, N.; Ghafourifar, P.; Szibor, M.; Fang, K.; Gaston, B. S-Nitrosylation of mitochondrial caspases. J. Cell Biol. 2001, 154, 1111−1116. (2) Rahman, M. A.; Senga, T.; Ito, S.; Hyodo, T.; Hasegawa, H.; Hamaguchi, M. S-Nitrosylation at cysteine 498 of c-Src tyrosine kinase 4956

dx.doi.org/10.1021/pr5002675 | J. Proteome Res. 2014, 13, 4942−4958

Journal of Proteome Research

Article

(22) Itzkowitz, S. H.; Yio, X. Inflammation and cancer IV. Colorectal cancer in inflammatory bowel disease: the role of inflammation. Am. J. Physiol.: Gastrointest. Liver Physiol. 2004, 287, G7−17. (23) Siegel, R.; Naishadham, D.; Jemal, A. Cancer statistics, 2012. Ca−Cancer J. Clin. 2012, 62, 10−29. (24) Kimura, H.; Miura, S.; Shigematsu, T.; Ohkubo, N.; Tsuzuki, Y.; Kurose, I.; Higuchi, H.; Akiba, Y.; Hokari, R.; Hirokawa, M.; Serizawa, H.; Ishii, H. Increased nitric oxide production and inducible nitric oxide synthase activity in colonic mucosa of patients with active ulcerative colitis and Crohn’s disease. Dig. Dis. Sci. 1997, 42, 1047− 1054. (25) Chhatwal, V. J. S.; Ngoi, S. S.; Chan, S. T. F.; Chia, Y. W.; Moochhala, S. M. Aberrant expression of nitric oxide synthase in human polyps, neoplastic colonic mucosa and surrounding peritumoral normal mucosa. Carcinogenesis 1994, 15, 2081−2085. (26) Leon-Bollotte, L.; Subramaniam, S.; Cauvard, O.; PlenchetteColas, S.; Paul, C.; Godard, C.; Martinez-Ruiz, A.; Legembre, P.; Jeannin, J. F.; Bettaieb, A. S-Nitrosylation of the death receptor Fas promotes Fas ligand-mediated apoptosis in cancer cells. Gastroenterology 2011, 140, 2009−2018. (27) Dall’Agnol, M.; Bernstein, C.; Bernstein, H.; Garewal, H.; Payne, C. M. Identification of S-nitrosylated proteins after chronic exposure of colon epithelial cells to deoxycholate. Proteomics 2006, 6, 1654−1662. (28) Wu, C.; Parrott, A. M.; Liu, T.; Jain, M. R.; Yang, Y.; Sadoshima, J.; Li, H. Distinction of thioredoxin transnitrosylation and denitrosylation target proteins by the ICAT quantitative approach. J. Proteomics 2011, 74, 2498−2509. (29) Paige, J. S.; Xu, G.; Stancevic, B.; Jaffrey, S. R. Nitrosothiol reactivity profiling identifies S-nitrosylated proteins with unexpected stability. Chem. Biol. 2008, 15, 1307−1316. (30) Murray, C. I.; Uhrigshardt, H.; O’Meally, R. N.; Cole, R. N.; Van Eyk, J. E. Identification and quantification of S-nitrosylation by cysteine reactive tandem mass tag switch assay. Mol. Cell. Proteomics 2012, 11, M111.013441. (31) Forrester, M. T.; Thompson, J. W.; Foster, M. W.; Nogueira, L.; Moseley, M. A.; Stamler, J. S. Proteomic analysis of S-nitrosylation and denitrosylation by resin-assisted capture. Nat. Biotechnol. 2009, 27, 557−559. (32) Benhar, M.; Thompson, J. W.; Moseley, M. A.; Stamler, J. S. Identification of S-nitrosylated targets of thioredoxin using a quantitative proteomic approach. Biochemistry 2010, 49, 6963−6969. (33) Zhou, X.; Han, P.; Li, J.; Zhang, X.; Huang, B.; Ruan, H.-Q.; Chen, C. ESNOQ, proteomic quantification of endogenous Snitrosation. PLoS One 2010, 5, e10015. (34) Kohr, M. J.; Aponte, A. M.; Sun, J.; Wang, G.; Murphy, E.; Gucek, M.; Steenbergen, C. Characterization of potential S-nitrosylation sites in the myocardium. Am. J. Physiol.: Heart Circ. Physiol. 2011, 300, H1327−H1335. (35) Kohr, M. J.; Sun, J.; Aponte, A.; Wang, G.; Gucek, M.; Murphy, E.; Steenbergen, C. Simultaneous measurement of protein oxidation and S-nitrosylation during preconditioning and ischemia/reperfusion injury with resin-assisted capture. Circ. Res. 2011, 108, 418−426. (36) Zhang, X.; Huang, B.; Chen, C. SNO spectral counting (SNOSC), a label-free proteomic method for quantification of changes in levels of protein S-nitrosation. Free Radical Res. 2012, 46, 1044− 1050. (37) Chen, Y.-J.; Ku, W.-C.; Lin, P.-Y.; Chou, H.-C.; Khoo, K.-H.; Chen, Y.-J. S-Alkylating labeling strategy for site-specific identification of the S-nitrosoproteome. J. Proteome Res. 2010, 9, 6417−6439. (38) Tsou, C.-C.; Tsai, C.-F.; Tsui, Y.-H.; Sudhir, P.-R.; Wang, Y.-T.; Chen, Y.-J.; Chen, J.-Y.; Sung, T.-Y.; Hsu, W.-L. IDEAL-Q, an automated tool for label-free quantitation analysis using an efficient peptide alignment approach and spectral data validation. Mol. Cell. Proteomics 2010, 9, 131−144. (39) Cheng, J.-Y.; Meng, C.-L.; Chao, C.-F.; Gau, S.-D.; Lin, J.-C. Human papillomavirus type-related DNA and c-myc oncogene alterations in colon cancer cell lines. Dis. Colon Rectum 1991, 34, 469−474.

(40) Vizcaíno, J. A.; Côté, R. G.; Csordas, A.; Dianes, J. A.; Fabregat, A.; Foster, J. M.; Griss, J.; Alpi, E.; Birim, M.; Contell, J.; O’Kelly, G.; Schoenegger, A.; Ovelleiro, D.; Pérez-Riverol, Y.; Reisinger, F.; Ríos, D.; Wang, R.; Hermjakob, H. The Proteomics Identifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 2013, 41, D1063−D1069. (41) Lee, T.-Y.; Chen, Y.-J.; Lu, T.-C.; Huang, H.-D.; Chen, Y.-J. SNOSite: exploiting maximal dependence decomposition to identify cysteine S-nitrosylation with substrate site specificity. PLoS One 2011, 6, e21849. (42) Lee, T.-Y.; Lin, Z.-Q.; Hsieh, S.-J.; Bretaña, N. A.; Lu, C.-T. Exploiting maximal dependence decomposition to identify conserved motifs from a group of aligned signal sequences. Bioinformatics 2011, 27, 1780−1787. (43) Suliman, H. B.; Babiker, A.; Withers, C. M.; Sweeney, T. E.; Carraway, M. S.; Tatro, L. G.; Bartz, R. R.; Welty-Wolf, K. E.; Piantadosi, C. A. Nitric oxide synthase-2 regulates mitochondrial HSP60 chaperone function during bacterial peritonitis in mice. Free Radical Biol. Med. 2011, 48, 736−746. (44) Lam, Y. W.; Yuan, Y.; Isaac, J.; Babu, C. V. S.; Meller, J.; Ho, S.M. Comprehensive identification and modified-site mapping of Snitrosylated targets in prostate epithelial cells. PLoS One 2010, 5, e9075. (45) Wu, C.; Liu, T.; Chen, W.; Oka, S.-i.; Fu, C.; Jain, M. R.; Parrott, A. M.; Baykal, A. T.; Sadoshima, J.; Li, H. Redox regulatory mechanism of transnitrosylation by thioredoxin. Mol. Cell. Proteomics 2010, 9, 2262−2275. (46) Mitsumoto, A.; Kim, K.-R.; Oshima, G.; Kunimoto, M.; Okawa, K.; Iwamatsu, A.; Nakagawa, Y. Glyoxalase I is a novel nitric-oxideresponsive protein. Biochem. J. 1999, 344, 837−844. (47) Hamelin, C.; Cornut, E.; Poirier, F.; Pons, S.; Beaulieu, C.; Charrier, J.-P.; Haïdous, H.; Cotte, E.; Lambert, C.; Piard, F.; AtamanÖ nal, Y.; Choquet-Kastylevsky, G. Identification and verification of heat shock protein 60 as a potential serum marker for colorectal cancer. FEBS J. 2011, 278, 4845−4859. (48) Cappello, F.; Bellafiore, M.; Palma, A.; David, S.; Marcianò, V.; Bartolotta, T.; Sciumè, C.; Modica, G.; Farina, F.; Zummo, G.; Bucchieri, F. 60KDa chaperonin (HSP60) is over-expressed during colorectal carcinogenesis. Eur. J. Histochem. 2003, 47, 105−110. (49) Duncan, R.; Carpenter, B.; Main, L. C.; Telfer, C.; Murray, G. I. Characterisation and protein expression profiling of annexins in colorectal cancer. Br. J. Cancer 2008, 98, 426−433. (50) Bakalova, R.; Zhelev, Z.; Aoki, I.; Saga, T. Tissue redox activity as a hallmark of carcinogenesis: from early to terminal stages of cancer. Clin. Cancer Res. 2013, 19, 2503−2517. (51) Raffel, J.; Bhattacharyya, A. K.; Gallegos, A.; Cui, H.; Einspahr, J. G.; Alberts, D. S.; Powis, G. Increased expression of thioredoxin-1 in human colorectal cancer is associated with decreased patient survival. J. Lab. Clin. Med. 2003, 142, 46−51. (52) López-Sánchez, L. M.; López-Pedrera, C.; Rodríguez-Ariza, A. Proteomic approaches to evaluate protein S-nitrosylation in disease. Mass Spectrom. Rev. 2014, 33, 7−20. (53) Han, C.-L.; Chen, J.-S.; Chan, E.-C.; Wu, C.-P.; Yu, K.-H.; Chen, K.-T.; Tsou, C.-C.; Tsai, C.-F.; Chien, C.-W.; Kuo, Y.-B.; Lin, P.-Y.; Yu, J.-S.; Hsueh, C.; Chen, M.-C.; Chan, C.-C.; Chang, Y.-S.; Chen, Y.J. An informatics-assisted label-free approach for personalized tissue membrane proteomics: case study on colorectal cancer. Mol. Cell. Proteomics 2011, 10, M110.003087. (54) Kim, S. F.; Huri, D. A.; Snyder, S. H. Inducible nitric oxide synthase binds, S-nitrosylates, and activates cyclooxygenase-2. Science 2005, 310, 1966−1970. (55) Cianchi, F.; Cortesini, C.; Fantappiè, O.; Messerini, L.; Schiavone, N.; Vannacci, A.; Nistri, S.; Sardi, I.; Baroni, G.; Marzocca, C.; Perna, F.; Mazzanti, R.; Bechi, P.; Masini, E. Inducible nitric oxide synthase expression in human colorectal cancer: correlation with tumor angiogenesis. Am. J. Pathol. 2003, 162, 793− 801. (56) Seth, D.; Stamler, J. S. The SNO-proteome: causation and classifications. Curr. Opin. Chem. Biol. 2011, 15, 129−136. 4957

dx.doi.org/10.1021/pr5002675 | J. Proteome Res. 2014, 13, 4942−4958

Journal of Proteome Research

Article

(75) Atkins, J. L.; Day, B. W.; Handrigan, M. T.; Zhang, Z.; Pamnani, M. B.; Gorbunov, N. V. Brisk production of nitric oxide and associated formation of S-nitrosothiols in early hemorrhage. J. Appl. Physiol. 2006, 100, 1267−1277. (76) Carmichael, A. J.; Steel-Goodwin, L.; Gray, B.; Arroyo, C. M. Nitric oxide interaction with lactoferrin and its production by macrophage cells studied by EPR and spin trapping. Free Radical Res. Commun. 1993, 19, S201−S209. (77) Hendrixson, D. R.; Qiu, J.; Shewry, S. C.; Fink, D. L.; Petty, S.; Baker, E. N.; Plaut, A. G.; St. Geme, J. W. Human milk lactoferrin is a serine protease that cleaves Haemophilus surface proteins at argininerich sites. Mol. Microbiol. 2003, 47, 607−617.

(57) Lillig, C. H.; Holmgren, A. Thioredoxin and related molecules− from biology to health and disease. Antioxid. Redox Signaling 2007, 9, 25−47. (58) Holmgren, A.; Lu, J. Thioredoxin and thioredoxin reductase: current research with special reference to human disease. Biochem. Biophys. Res. Commun. 2010, 396, 120−124. (59) Fernandes, A. P.; Capitanio, A.; Selenius, M.; Brodin, O.; Rundlöf, A.-K.; Björnstedt, M. Expression profiles of thioredoxin family proteins in human lung cancer tissue: correlation with proliferation and differentiation. Histopathology 2009, 55, 313−320. (60) Zhang, T.; Liu, H.; Zhu, C.; Briggs, K.; Kang, Y. a.; Fleming, J. A.; Curley, S. A. Silencing thioredoxin induces liver cancer cell senescence under hypoxia. Hepatol. Res. 2012, 42, 706−713. (61) Santel, T.; Pflug, G.; Hemdan, N. Y. A.; Schäfer, A.; Hollenbach, M.; Buchold, M.; Hintersdorf, A.; Lindner, I.; Otto, A.; Bigl, M.; Oerlecke, I.; Hutschenreuter, A.; Sack, U.; Huse, K.; Groth, M.; Birkemeyer, C.; Schellenberger, W.; Gebhardt, R.; Platzer, M.; Weiss, T.; Vijayalakshmi, M. A.; Krüger, M.; Birkenmeier, G. Curcumin inhibits glyoxalase 1a possible link to its anti-inflammatory and antitumor activity. PLoS One 2008, 3, e3508. (62) Birkenmeier, G.; Stegemann, C.; Hoffmann, R.; Günther, R.; Huse, K.; Birkemeyer, C. Posttranslational modification of human glyoxalase 1 indicates redox-dependent regulation. PLoS One 2012, 5, e10399. (63) Mitsumoto, A.; Kim, K.-R.; Oshima, G.; Kunimoto, M.; Okawa, K.; Iwamatsu, A.; Nakagawa, Y. Nitric oxide inactivates glyoxalase I in cooperation with glutathione. J. Biochem. 2000, 128, 647−654. (64) Liu, J.-W.; Shen, J.-J.; Tanzillo-Swarts, A.; Bhatia, B.; Maldonado, C. M.; Person, M. D.; Lau, S. S.; Tang, D. G. Annexin II expression is reduced or lost in prostate cancer cells and its reexpression inhibits prostate cancer cell migration. Oncogene 2003, 22, 1475−1485. (65) Liu, L.; Enright, E.; Sun, P.; Tsai, S. Y.; Mehta, P.; Beckman, D. L.; Terrian, D. M. Inactivation of annexin II tetramer by Snitrosoglutathione. Eur. J. Biochem. 2002, 269, 4277−4286. (66) Pike, S. E.; Yao, L.; Setsuda, J.; Jones, K. D.; Cherney, B.; Appella, E.; Sakaguchi, K.; Nakhasi, H.; Atreya, C. D.; TeruyaFeldstein, J.; Wirth, P.; Gupta, G.; Tosato, G. Calreticulin and calreticulin fragments are endothelial cell inhibitors that suppress tumor growth. Blood 1999, 94, 2461−2468. (67) Gold, L. I.; Eggleton, P.; Sweetwyne, M. T.; Van Duyn, L. B.; Greives, M. R.; Naylor, S.-M.; Michalak, M.; Murphy-Ullrich, J. E. Calreticulin: non-endoplasmic reticulum functions in physiology and disease. FASEB J. 2010, 24, 665−683. (68) Wang, X.; Phelan, S. A.; Forsman-Semb, K.; Taylor, E. F.; Petros, C.; Brown, A.; Lerner, C. P.; Paigen, B. Mice with targeted mutation of peroxiredoxin 6 develop normally but are susceptible to oxidative stress. J. Biol. Chem. 2003, 278, 25179−25190. (69) Fisher, A. B. Peroxiredoxin 6: a bifunctional enzyme with glutathione peroxidase and phospholipase A2 activities. Antioxid. Redox Signaling 2011, 15, 831−844. (70) Trudel, S.; Kelly, M.; Fritsch, J.; Nguyen-Khoa, T.; Thérond, P.; Couturier, M.; Dadlez, M.; Debski, J.; Touqui, L.; Vallée, B.; Ollero, M.; Edelman, A.; Brouillard, F. Peroxiredoxin 6 fails to limit phospholipid peroxidation in lung from cf tr-knockout mice subjected to oxidative challenge. PLoS One 2009, 4, e6075. (71) Foster, M. W.; Hess, D. T.; Stamler, J. S. Protein S-nitrosylation in health and disease: a current perspective. Trends Mol. Med. 2009, 15, 391−404. (72) Kapralov, A.; Vlasova, I. I.; Feng, W.; Maeda, A.; Walson, K.; Tyurin, V. A.; Huang, Z.; Aneja, R. K.; Carcillo, J.; Bayır, H.; Kagan, V. E. Peroxidase activity of hemoglobin·haptoglobin complexes. Covalent aggregation and oxidative stress in plasma and macrophages. J. Biol. Chem. 2009, 284, 30395−30407. (73) Gow, A. J. Nitric oxide, hemoglobin, and hypoxic vasodilation. Am. J. Respir. Cell Mol. Biol. 2005, 32, 479−482. (74) Angelo, M.; Singel, D. J.; Stamler, J. S. An S-nitrosothiol (SNO) synthase function of hemoglobin that utilizes nitrite as a substrate. Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 8366−8371. 4958

dx.doi.org/10.1021/pr5002675 | J. Proteome Res. 2014, 13, 4942−4958