Quantitative Proteomic Approaches for Analysis of Protein S

Nov 6, 2015 - †Department of Pathology and Anatomical Sciences and ‡Center for Translational Neuroscience, University of Missouri School of Medici...
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Quantitative proteomic approaches for analysis of protein S-nitrosylation Zhe Qu, C. Michael Greenlief, and Zezong Gu J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.5b00857 • Publication Date (Web): 06 Nov 2015 Downloaded from http://pubs.acs.org on November 17, 2015

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Quantitative proteomic approaches for analysis of protein S-nitrosylation Zhe Qu,†, ‡ C Michael Greenlief,§ and Zezong Gu*,†,‡,⊥ †



Department of Pathology and Anatomical Sciences, Center for Translational Neuroscience, University of Missouri School of Medicine, Columbia, Missouri 65212, United States § Department of Chemistry, University of Missouri College of Arts and Science, Columbia, Missouri 65211, United States ⊥

Harry S. Truman Veterans Hospital, Columbia, Missouri 65212, United States

* Corresponding author: Dr. Zezong Gu, Department of Pathology and Anatomical Sciences, University of Missouri School of Medicine, M263 Medical Science Building, One Hospital Drive, Columbia, MO 65212, USA. Tel: 573884-3880. Fax: 573-884-4612. E-mail: [email protected].

ABSTRACT S-Nitrosylation is a redox-based post-translational modification of a protein in response to nitric oxide (NO) signaling, and it participates in a variety of processes in diverse biological systems. The significance of this type of protein modification in health and diseases is increasingly recognized. In the central nervous system, aberrant S-nitrosylation, due to excessive NO production, is known to cause protein misfolding, mitochondrial dysfunction, transcriptional dysregulation, and neuronal death. This leads to an altered physiological state and consequently contributes to pathogenesis of neurodegenerative disorders. To date, much effort has been made to understand the mechanisms underlying protein S-nitrosylation, and several approaches have been developed to unveil S-nitrosylated proteins from different organisms. Interest in determining the dynamic changes of protein S-nitrosylation under different physiological and pathophysiological conditions has underscored the need for the development of quantitative proteomic approaches. Currently, both gel-based and gel-free mass spectrometry-based quantitative methods are widely used, and they each have advantages and disadvantages but may also be used together to produce complimentary data. This review evaluates current available quantitative proteomic techniques for the analysis of protein S-nitrosylation and highlights recent advances, with emphasis on applications in neurodegenerative diseases. An important goal is to provide a comprehensive guide of feasible quantitative proteomic methodologies for examining protein S-nitrosylation in research to yield insights to disease mechanisms, diagnostic biomarkers and drug discovery.

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KEYWORDS: S-nitrosylation, nitric oxide, quantitative proteomics, neurodegenerative diseases

INTRODUCTION Nitric oxide (NO) is an important signal molecule produced endogenously by NO synthases (NOS) and functions in diverse biological systems. NO acts, in large part, through selective redox-based covalent modification of cysteine residue(s) of target proteins to form S-nitrosothiols, termed protein S-nitrosylation. This evolutionally conserved, prototypic, redox-based protein post-translational modification regulates a wide-range of cellular functions and signaling processes, such as relaxation/vasodilation, neurotransmission, cell cycle regulation, cellular trafficking, transcriptional regulation, and apoptosis (1-3). NO is maintained at low levels in cells under physiological conditions, allowing it to fulfill its regulatory role in diverse signaling pathways. In contrast, excessive NO production induced by environmental toxins triggers nitrosative stress and dysregulation of protein S-nitrosylation, and consequently leads to a progression of diseases, including cancer, diabetes, asthma, heart failure, and neurodegeneration (1, 4-6). Recent studies have revealed that aberrant S-nitrosylation of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (7), protein-disulfide isomerase (PDI) (8), X-linked inhibitor of apoptosis (XIAP) (9, 10), dynamin-related protein-1 (Drp-1) (11), E3 ubiquitin-protein ligase parkin (12), and myocyte enhancer factor 2 (MEF2) (13), all are associated with neuronal death in neurodegenerative diseases. Substantial evidence indicates that S-nitrosylation is specific and precisely regulated in cells (14-16). Understanding the molecular mechanisms of NO signaling and the regulatory roles of S-nitrosylation requires identification of S-nitrosylated proteins (SNO-proteins) and their modification sites. However, due to the low abundance and the labile nature of the S-nitrosothiol bond for protein S-nitrosylation, detection of the modification has been a challenging task. Over the last two decades, a variety of strategies have been established, including direct and indirect methods of detection. A biotin switch technique (BST) was developed and widely used, in which S-nitrosylated cysteine (SNO-Cys) is indirectly detected by switchlabeling with a reversible biotin-HPDP reagent (17). Subsequent to the BST, many variants were introduced with improved specificity, sensitivity, and throughput ability (18). Further, the increasing need to monitor S-nitrosylation dynamics in complex biological samples under different conditions has promoted the development of quantitative proteomic approaches, which can be classified into two categories, gelbased and gel-free mass spectrometry (MS)-based strategies. This review summarizes major approaches for investigating protein S-nitrosylation and highlights recent advances in quantitative proteomics along with their applications in mechanistic studies of neurodegenerative diseases.

DIRECT METHODS FOR DETECTION OF S-NITROSYLATION Early studies for the analysis of S-nitrosylation were carried out largely with purified proteins or peptides for their NO-mediated responses using spectrographic methods, including the DAN assay, the Saville reaction and the chemiluminescence assay, to detect NO species released from SNO-proteins (19-22). Those methods are at the low micromolar (DAN assay and Saville reaction) or nanomolar (chemiluminescence assay) range of detection and the former method, in particular, requires relatively large amounts of the purified materials. Due to the low abundance, the instability of the S-nitrosylation and the compatibility issue of sample preparation, direct mass spectrometric detection of this modification faces great challenges. With electrospray ionization quadrupole time-of-flight (ESI-QToF) MS under gentle conditions that safeguard the integrity of the S-nitrosothiol bond, direct detection of SNO-Cys has proved feasible by recognizing a mass shift of 29 Da due to bound NO (23, 24). Low-energy electron transfer dissociation (ETD) or electron capture dissociation (ECD) coupled with a Fourier transform ion cyclotron resonance mass spectrometer is also able to keep the side-chains intact on the peptide backbone providing possibility for analysis of protein S-nitrosylation (25). Another unique feature of ETD and ECD tandem MS is that they are able to produce 2

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an almost complete series of ions for peptide backbone fragmentation and thus extensive peptide sequence information. Transnitrosylation of XIAP, which functions as an E3 ubiquitin ligase targeting caspases for degradation to promote cell injury and death, was first observed by our group using a LTQ Orbitrap XL mass spectrometer coupled with ETD (10). Such direct observation by tandem MS avoids derivatization steps that can produce false positive signals. However, this method is limited to synthetic peptides or purified recombinant proteins and not suitable for a large-scale proteomic analysis of S-nitrosylation in complex biological systems. Anti-S-nitrosocysteine antibodies are also available for the direct detection of S-nitrosylation on proteins. SNO-proteins can be determined by an anti-S-nitrosocysteine antibody on a 2-dimensional Western blot, and the corresponding spots are picked on a regularly stained gel for identification with MS. Alternatively, the antibody can be used to immunoprecipate SNO-proteins from a complex sample, and the eluates are then identified by MS (26). A drawback to this approach is that the efficiency and specificity of the immunoassays may hinder the outcome, and importantly, these methods cannot differentiate SNO-Cys sites in proteins. Direct detection of SNO-proteins can also be performed by chemical compounds that react with Snitrosothiol to form stable adducts. After blocking free thiols, reagents such as gold nanoparticles (27), organomercury compounds (28), and phosphine compounds (29, 30), react with S-nitrosothiol and thus the SNO-proteins can be captured via solid phase resin or affinity tagging for further MS identification. In the organomercury-based strategy, Doulias et al. directly and specifically label S-nitrosothiols with organomercury resin (MRC) or phenylmercury-polyethyleneglycol-biotin (mPEGb) to form stable mercurythiolate conjugates (28). After enriched by either MRC or avidin affinity capture, captured proteins or peptides are eluted by β-mercaptoethanol or performic acid. Performic acid can oxidize the resulting thiols to sulfonic acid, thus generating a MS signature for SNO-Cys site-mapping. Analysis of endogenous SNO-proteins using this organomercury-based approach identified 269 SNO-Cys sites in 136 SNO-proteins in wild-type mouse brain and revealed the regulatory role of nNOS-dependent protein S-nitrosylation in glutamatergic neurotransmission and metabolism in the central nervous system (31). While these strategies display the advantage of direct detection avoiding reduced efficacy and certain levels of false positives in the identification of SNO-proteins resulting from indirect detection, they all need to be extended for multiplexed quantitation of S-nitrosylation.

BIOTIN SWITCH TECHNIQUE (BST) AND BST-MODIFIED METHODS Indirect detection strategies selectively reduce S-nitrosothiol with a mild reducing reagent (e.g., ascorbate) and label the same site with another tag forming a stable bond for further detection, enrichment, and/or quantification. The biotin switch technique (BST) was introduced by Snyder’s group in 2001 and was a milestone for the detection of protein S-nitrosylation from a complex biological specimen (17). The assay comprises three major steps, including first blocking free thiols with a sulfhydryl-reactive reagent, such as S–methyl methanethiosulfonate (MMTS), then selectively reducing the S-nitrosothiols with ascorbate, and labeling with biotin-HPDP. The biotinylated SNO-proteins can be enriched via avidin-agarose affinity capture and then detected by immunoblotting or by MS analysis to investigate the S-nitrosoproteome. The BST method is highly specific for S-nitrosothiols (32) and has been widely used under both in vivo and in vitro conditions for the investigation of SNO-proteins. However, this method has relatively low throughput and is not capable of locating and quantifying SNO-Cys sites. Additionally, the biotin affinity enrichment may introduce false positive signals due to the existence of endogenous biotin-like molecules. Therefore, different strategies have been developed by modifying the BST method in order to enable SNOCys site mapping and improve throughput, sensitivity, and specificity. SNO-Cys site identification (SNOSID) introduces a proteolytic digestion step to BST after biotinylation of SNO-Cys and before avidin-agarose affinity capture, hence allowing peptide-level enrichment for better efficiency in subsequent liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis and permitting SNO-Cys site-mapping (33). However, the reversible labeling of biotin-HPDP is not compatible with a reduction/alkylation step before proteolysis and thus can cause inadequate protein digestion, 3

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hindering peptide detection efficiency during MS analysis. Moreover, since the biotin tagging on SNO-Cys is lost after elution from avidin-agarose in the reducing buffer, this SNOSID method is unable to specify the modification site when there is more than one cysteine present in an identified peptide. The S-alkylating labeling strategy presented by Chen et al. utilized iodoacetamide (IAM) as a blocking reagent instead of MMTS in BST to produce an irreversible, stable blockage of the free thiols and used (+)biotinyl-iodoacetamidyl-3,6-dioxaoctanediamine (EZ-Link Iodoacetyl-PEG2-biotin) as a tagging reagent substituting biotin-HPDP to irreversibly biotinylate SNO-Cys (34). In MS/MS analysis, the biotinylated cysteines are recognized by the mass shift of EZ-Link Iodoacetyl-PEG2-biotin (+414.2 Da) allowing unambiguous modification site assignment. With the irreversible tagging on the SNO-Cys, the disulfide bonds in the proteins can be further reduced with strong reducing reagents for sufficient protein denaturation and digestion, which benefits peptide enrichment efficiency and downstream LC-MS/MS analysis sensitivity. In a similar method, an irreversible biotinylation procedure by Huang et al. employs NEM for blocking free cysteine residues and biotin-maleimide or biotin-PEO-maleimide to label SNO-Cys (35). The BST method and its variations mentioned above have uncovered previously unknown SNO-proteins and their S-nitrosylation sites in diverse cellular processes. After knowing “what”, the next question would be “how much”. It is critical to assess the changes in protein S-nitrosylation level in different biological conditions for revealing NO signaling mechanisms and the regulatory effect of S-nitrosylation. However, the methods mentioned above have difficulty in providing quantitative information.

QUANTITATIVE PROTEOMIC APPROACHES TO STUDY PROTEIN SNITROSYLATION Protein S-nitrosylation has been shown to exert critical roles in a variety of cellular processes by regulating protein folding, ubiquitination, mitochondrial dynamics, and signal transduction (5, 8, 11, 12, 36). To explore the dynamic changes in levels of SNO-proteins under different physiological and pathophysiological conditions, global, unbiased, quantitative proteomic analyses of S-nitrosylation are highly needed. Various quantitative methods have been developed, and they can be classified into two main categories, gel-based quantification and gel-free MS-based strategies (Table 1).

Gel-based methods In combination with a two-dimensional gel electrophoresis (2-DE) technique, thiol-reactive fluorescence dyes are used to specifically label, detect, and quantify protein S-nitrosylation with fair sensitivity. A gelbased strategy for quantitative analysis of protein S-nitrosylation generally involves switch-labeling SNOCys with a thiol-reactive fluorescence dye, separation by 2-DE, quantification of fluorescence intensity to screen protein spots with differentially S-nitrosylated proteins, and protein identification by MS. A representative approach termed NitroDIGE reported by our group (37) uses the same blocking and reduction steps as the previously established BST. Free thiols are blocked by methylthiolation with MMTS and nitrosothiols on SNO-proteins are selectively reduced with ascorbate (Figure 1, A). However, the thiol labeling reagent biotin-HPDP in BST is replaced with the irreversible fluorescence-based maleimide reactive reagents, CyDye™ DIGE Fluor dyes (Cy3 and Cy5), which form a covalent bond with the nascent thiols reduced from SNO-Cys by ascorbate via a thioether linkage. This tagging is resistant to reducing environments and thus is not lost during sample processing and electrophoresis. The labeled protein samples are then resolved on two-dimensional difference in gel electrophoresis (2D-DIGE) gels. In previous reports, labeling with CyDye™ has been used to compare protein S-nitrosylation level in two different samples within one gel (38-41). While in the NitroDIGE method, an internal standard pooled from every sample in equal amounts is included for analysis of multiple samples across multiple gels with reduced inter-gel variation. The internal standard is labeled with either Cy3 or Cy5, and then run on each gel together with an experimental sample labeled with the other CyDye™ at a 1:1 ratio. The same internal standard is present in each gel and thus can be used for inter-gel normalization. After 2D-DIGE, changes in protein S-nitrosylation levels are quantified by comparing the fluorescence intensity of Cy3 and Cy5 with 4

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SameSpots or DeCyder 2-D Differential Analysis Software. Fluorescence intensity of a sample is normalized to the internal standard on each gel and then compared with other samples across different gels. Spots (SNO-proteins) with significant fold changes between control and treatment samples are excised on a corresponding zinc stained gel for protein identification by LC-MS/MS analysis. Using NitroDIGE, we identified 67, 13, and 78 proteins as targets for S-nitrosylation in BV-2 microglial cells after exposure to Snitrosocysteine (SNOC), LPS, and epigallocatechin-3-gallate (EGCG), respectively. Bioinformatic analysis with the identified SNO-proteins suggested that EGCG attenuates S-nitrosylation of proteins after lipopolysaccharide (LPS)-induced microglial activation primarily by modulation of the Nrf2 (nuclear factor erythroid 2-related factor 2)-mediated oxidative stress response. These results demonstrated that NitroDIGE is suitable for quantifying protein S-nitrosylation in both in vitro and in vivo samples. The method can be applied to the examination of dynamic S-nitrosylation changes among normal, disease, and drug treated groups, finding biomarkers, potential therapeutic targets, and the mechanisms underlying disease and drug treatment. SNO-DIGE, another gel-based method employing CyDyeTM, uses a different way to prepare the internal standard by pooling equal amounts of protein from only the control samples (42); instead of from all samples including control and treated. If the S-nitrosylation level of a protein in control samples is too low to detect but is high in treated samples, it can cause problems in normalizing a treated sample on a gel to the internal standard pooled only from the control samples. Besides quantifying S-nitrosylation via comparing fluorescence intensity between Cy3 and Cy5 that are tagged to SNO-proteins, a fluorescence saturation approach (SNOFlo) can be used with only one dye, BODIPY FL-maleimide (BD) (43). In this method, a protein sample is divided into two equal fractions that are treated and untreated with ascorbate, respectively, prior to excessive BD labeling. After 2-DE separation and fluorescence quantification, alterations of S-nitrosylation status between control and experimental samples are determined by the fractions untreated with ascorbate and then normalized to protein expression changes that are indicated by the fractions treated with ascorbate. This approach introduces a unique concept by quantifying Snitrosylation changes relative to protein abundance. However, since samples are run separately on different gels, gel-to-gel variations must be carefully controlled. 2D-DIGE resolves proteins based on isoelectric point and molecular weight. Numerous proteins can be separated by 2D-DIGE due to its high separation efficiency. 2D-DIGE also offers reproducible, confident protein identifications, and is suitable for large-scale screening. Combined with the fluorescence switchlabeling of SNO-Cys, 2D-DIGE and LC-MS/MS analysis, gel-based methods for quantifying protein Snitrosylation are sensitive and utilize a smaller sample size than that is normally required in BST, and without the enrichment step utilized in gel free MS-based quantification approach. Gel-based methods also enable low abundance proteins to be detected. In NitroDIGE, comparing S-nitrosylation levels of multiple samples with biological replicates on different 2D-DIGE gels simultaneously by including an internal standard can help to overcome both biological and technical variations. However, gel-based methods have the inherent limitations of 2D-DIGE, including the difficulty of identifying hydrophobic proteins or those with high pIs. The methodology is also labor intensive. Additionally, multiple proteins can be identified from a protein spot with high confidence, thus requiring additional experimental verification to confirm which protein(s) is(are) indeed S-nitrosylated. On the other hand, it is also possible to detect a protein in multiple protein spots due to other post-translational modifications, making it difficult to determine the Snitrosylation level change of the protein. Most importantly, the current gel-based methods have difficulties in specifying SNO-Cys site (44), although it has been proved possible (45), in part due to the very limited amount of sample that is extracted from gel for SNO-Cys mapping.

Gel-free MS-based methods Recent improvements in MS instrumentation and the introduction of robust quantitative MS-based techniques have facilitated proteomic analyses on a larger scale, with higher throughput, and with better sensitivity. Consequently, the development of gel-free MS-based methods for quantitative proteomic analysis of S-nitrosylation have advanced greatly over the last few years. Isotopic labeling and isobaric labeling are the two strategies used to switch label, map and quantify SNO-Cys. Tens of thousands of peptides from thousands of proteins can be quantitatively profiled within a single experiment. These methods have been successfully applied to identify unknown SNO-proteins, quantitate S-nitrosylation level 5

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changes, and dissect signaling pathways in different biological samples.

Isotopic labeling techniques Stable heavy isotopes, such as 13C, 15N, 18O, and 2H, are widely used for quantitative proteomic analysis. These isotopes are incorporated into proteins or peptides through chemical, metabolic or enzymatic reactions. Within a MS spectrum, the intensities of mass-shifted peaks resulting from paired ‘‘light’’ and ‘‘heavy’’ ions are used for relative peptide abundance quantification. Representative isotope labeling techniques include isotope coded affinity tags (ICAT; chemical labeling) and stable labeling by amino acids in cell culture (SILAC; metabolic labeling). The ICAT labeling technique was first developed for quantitation of protein levels (46). The original ICAT reagents consist of a thiol-reactive group for labeling Cys residues in proteins, an isotopic linker containing hydrogen (1H) or deuterium (2H) to generate a mass difference between ICAT-heavy and ICAT-light reagents, and a biotin tag moiety for peptide affinity enrichment. Due to the drawbacks caused by deuterium and the biotin tag in the ICAT reagents during MS analysis, the second-generation ICAT reagents were created by incorporating an acid-cleavable bond and by substituting 13C for 12C in the ICATheavy reagent rather than 2H for 1H (47). After labeling with ICAT-heavy and ICAT-light reagents, respectively, two distinct protein samples are combined, digested, enriched by biotin-avidin affinity chromatography, and quantified at the MS level based on paired light and heavy peptides with a mass difference of 9 Da. The biotin tag is removed by trifluoroacetic acid treatment before MS analysis. Coupled with LC-MS/MS, the ICAT labeling technique has been used to quantify proteins on a large scale with good accuracy (48). Recently, this ICAT labeling technique was utilized to locate SNO-Cys and quantify differential levels of SNO-proteins on a global scale by substituting ICAT reagents for biotin-HPDP in the BST method (49-53). Using this strategy, 50 SNO-proteins were identified by the ICAT method from mouse heart tissue in vitro treated with H2O2 (50). Thirty-seven peptides were found with altered Snitrosylation levels between normal and type 2 diabetic mice (51). In Arabidopsis suspension cells under saline stress, 3 peptides were found with increased S-nitrosylation levels and 14 underwent denitrosylation (53). ICAT proves to be an effective tool for the precise localization of SNO-Cys and large-scale quantification of endogenous SNO-proteins. Isotopic SNOCAP (S-nitrosothiol capture reagent), a similar reagent to ICAT, was used to compare protein S-nitrosylation level changes between two treatments (54). SILAC is a metabolic labeling technique that has been used for global proteome quantification (55, 56). In this method, cells are cultured in a growth medium containing light or heavy isotope-coded essential amino acids, arginine and lysine, which are consequently incorporated into all cellular proteins after several generations. Different isotope-coded samples are combined immediately after cell lysis and processed for LC-MS/MS analysis, where peptides are recognized by the mass shift between light and heavy stable isotopes and quantified by peak intensities. SILAC is compatible for all cell culture conditions and has been recently used in animals (57-59). SILAC-based quantification has been used in conjunction with the BST method for proteome-wide analyses of S-nitrosylation (60, 61). Benhar et al. found 46 SNO-proteins as substrates of a denitrosylase thioredoxin 1 from Jurkat cells, and Zhou et al. identified 27 SNO-proteins in LPS/ IFNγ-induced RAW264.7 cells. SILAC demonstrates good quantitative accuracy and high identification rates. However, the incorporation of the isotope-coded amino acids to protein samples is time consuming and costly. Additionally, it is difficult to specify SNO-Cys using this method, as the biotin tags on the modification sites are lost under reducing conditions before MS analysis. As discussed above, isotope labeling strategies enable accurate relative quantification in large-scale screening of the S-nitrosoproteome, but they are limited to the analysis of two samples at a time. Moreover, comparing peptide abundance at MS level raises concerns about errors introduced by the shift of chromatographic retention times between the light and heavy labeled peptides during RP-LC separation (62).

Isobaric labeling methods Unlike isotopic reagents, isobaric reagents have identical molecular weight and chemical structures for chromatographic separation, thus increasing quantification accuracy. Moreover, isobaric-labeled peptides are distinguishable at the MS/MS level by their reporter ions after peptide fragmentation. The peptides are

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quantified by the relative intensities of the reporter ions. A set of isobaric reagents allows multiplexed quantification of protein samples in a single LC−MS/MS analysis, permitting simultaneous comparisons of multiple biological samples. iTRAQ (isobaric tags for relative and absolute quantification) and TMT (tandem mass tag) are the isobaric reagents commonly used for MS-based quantification of protein abundance. iTRAQ labeling is a popular approach widely used for multiplexed quantitation of a global proteome. The amine-reactive iTRAQ reagents consist of three components: reporter, balance, and reactive chemical groups. The mass of each of the four reagents is identical in a MS scan, but distinct reporter ions are generated in an MS/MS spectrum at m/z 114, 115, 116, and 117, respectively, whose intensities are used for the quantitation of their labeled peptides. 8-plexed iTRAQ reagents (MS/MS reporter ions at m/z 113, 114, 115, 116, 117, 118, 119, and 121, respectively) are now available. Peptides in 4 or 8 distinct samples are separately labeled on N-terminals and/or lysine residues (under defined conditions) by the amine reactive group on the iTRAQ reagent, combined and analyzed by LC-MS/MS. In a resin-assisted capture (SNO-RAC) strategy, iTRAQ was introduced for quantification of S-nitrosylation and denitrosylation (63). By modifying the BST method, Forrester et al. first blocked free cysteine residues by methylthiolation and then utilized a thiol-reactive resin to label and pull-down the ascorbate-reduced SNO-proteins. The captured proteins were proteolyzed and labeled by iTRAQ on-resin followed by DTT elution for LCMS/MS analysis. The SNO-RAC method successfully revealed the dynamic changes of protein Snitrosylation in HEK cells exposed to physiological NO donor at various time points. In particular, SNORAC demonstrates improved sensitivity for detection of high-mass (>100 kDa) SNO-proteins. SNO-RAC allows a simultaneous comparative S-nitrosoproteome study of multiple samples. The number of samples is only limited by the number of iTRAQ multiplex reagents available. The use of thiol-reactive resin in SNORAC simplifies the labeling and purification procedures thus requiring fewer steps than the original BST method. However, re-tagging with iTRAQ in SNO-RAC for quantification adds an extra step, and this amine labeling of iTRAQ neither indicates SNO-Cys on SNO-peptide nor excludes non-specific detections, since there is no mass signature on the modification site. Moreover, there is also a concern raised by the relatively large sphere size of the thiol-reactive resins, which possibly affect their accessibility to the SNOCys sites buried within the structure of proteins (64). Nevertheless, the SNO-RAC method has been adapted to various studies, such as the investigation of S-nitroso-CoA-mediated protein S-nitrosylation in yeast (65) and the analysis of S-nitrosoproteome in E.histolytica trophozoites treated with a NO donor, SNOC (66). Various TMT isobaric reagents are commercially available, including amine-reactive, carbonyl-reactive, and cysteine-reactive tags. Sixplex cysTMT is cysteine thiol-reactive and was applied to analysis of protein S-nitrosylation and oxidative modification in response to SNOC (67). Utilizing isobaric sixplex cysTMT reagents in the BST-based cysTMT switch assay achieves both multiplex quantification and SNO-Cys sitemapping. However, the cysTMT labeling of SNO-proteins is reversible and thus not compatible with reduction/alkylation before trypsin digestion for sufficient protein denaturation. This likely results in incomplete digestion of complicated protein samples therefore hindering the efficiency of MS detection. Using the cysTMT switch assay, 25 SNO-Cys sites were detected in cells exposed to SNOC (200 µM). Due to the above-mentioned reversible labeling, the cysTMT reagents were soon replaced by thiol-reactive iodoacetyl tandem mass tag (iodoTMT) sixplex reagents. Each isobaric iodoTMTsixplex reagent within a set has the same nominal mass and consists of a thiol-reactive iodoacetyl group, a MS-neutral spacer arm, and a MS/MS reporter. During MS/MS analysis, the isobaric mass tags can be cleaved by collisionalinduced dissociation (CID) or ETD generating reporter ions with unique m/z of 126−131. In an iodoTMT switch assay (ISA) we recently developed (Figure 1, B), the iodoTMT reagent is used to replace biotinHPDP in the BST as SNO-Cys specific labeling reagent (68). Unlike cysTMT, the iodoTMT reagents irreversibly label cysteine thiols resulting in a stable tagging modification even under strong reducing environments. The iodoTMT-labeled proteins can be detected with an anti-TMT antibody or subjected to LC-MS/MS analysis. Six different protein samples labeled individually by iodoTMTsixplex reagents are combined, digested and enriched by anti-TMT antibody resin. The iodoTMT-labeled peptides are specifically pulled down by a competitive elution buffer containing a TMT structural analogue rather than the acidic buffer used in cysTMT switch assay. During MS/MS analysis of the eluted peptides, SNO-Cys sites are indicated on MS spectra by mass increment of 329 Da from the iodoTMT tag and quantified based 7

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on the intensities of the reporter ions (126–131 Da) in MS/MS spectra after fragmentation. The efficiency of ISA in defining the functional S-nitrosoproteome has been demonstrated in three different biological applications. We identified 90 and 38 SNO-Cys sites from 68 and 30 proteins that were differentially S-nitrosylated in response to SNOC (for in vitro condition) and LPS (for in vivo condition), respectively, in BV-2 microglial cells. We further examined the effects of a botanical compound, S-allyl cysteine from aged garlic extract, on protein S-nitrosylation in LPS-stimulated microglial cells, and found that the S-nitrosylation level of 46 proteins were altered, protecting cells from nitrosative stress. Other methods using similar physiological or pathological conditions to induce S-nitrosylation identified up to 90 SNO-Cys sites (33, 67, 69). By comparison, ISA displayed high sensitivity resulting from its robust workflow, with irreversible iodoTMT mutiplex labeling, and the use of competitive elution for anti-TMT peptide enrichment. Notably, high doses of NO donors, beyond the range of physiological NO concentrations, were used in previous investigations (34, 63). Even though a large number of SNO-proteins were identified, the biological relevance of these data and the sensitivities of these methods under in vivo conditions remain uncertain. Databases using these data to conduct consensus motif analysis and site prediction might be less accurate. So far, both the linear consensus motif flanking the specific cysteine residue and the spatial relationship with its surrounding amino acid residues promoting protein Snitrosylation remain elusive. It seems that the environments surrounding the modified cysteine residues possess diverse features, since multiple molecular mechanisms exist for forming protein S-nitrosylation. Several factors have been thought to determine S-nitrosylation, including acid-base motif, hydrophobic residues, and solvent accessibility (28, 70-72). Most recently, a conserved I/L-X-C-X2-D/E motif was found necessary and sufficient for S-nitrosylation mediated by a S-nitrosylase complex consisting of inducible NOS (iNOS), S100A8, and S100A9 (73). Motif analysis by the Motif-X algorithm (74) using highly confident data—the 133 SNO-Cys sites identified by ISA in BV-2 microglial cells—demonstrated a novel consensus sequence motif (MxxC), in addition to the conventional acid−base and hydrophobic motifs (Figure 2 and Table 2). These results suggest that the proximal polar amino acids may also play a role in selective cysteine S-nitrosylation (68). Taken together, ISA is an effective tool for investigation of protein S-nitrosylation with several unique features. Up to six different samples, 3 in duplicates or 2 in triplicates can be analyzed at a time, promoting reliable and informative quantification of SNO-proteins from complex biological samples under different conditions. The irreversible labeling of iodoTMT reagents on SNO-Cys permits effective peptide digestion and acts as a MS signature, enabling unambiguous SNO-Cys mapping and excluding potential false positive signals from nonspecific peptides. S-Nitrosylated peptide (SNO-peptide) enrichment using high affinity anti-TMT chromatography with competitive elution improves the efficiency of identification of SNO-peptides. With increased throughput, high specificity, high sensitivity, site-mapping and multiplex quantification ability, ISA meets many of the demands for protein S-nitrosylation studies. Currently, simultaneous comparison of S-nitrosylation samples is limited to eight by 8-plexed iTRAQ reagents and six by iodoTMTsixplex reagents. If one is handling more samples, an internal standard from all samples can be pooled with equal amounts and included in each experiment. Label-free approaches are another solution. The SNO-RAC method has been coupled to label-free proteomic analysis for relative quantification of the increased S-nitrosylation observed under myocardial ischemic preconditioning in mice (44). However, label-free approaches require careful control of the conditions used for each experiment to maintain reproducibility. Gel-free MS-based methods can assess relative changes in S-nitrosylation for hundreds of SNO-proteins from multiple samples in a single experiment, including membrane proteins that are not assessable by gelbased methods. Most importantly, SNO-Cys are unambiguously specified and quantified by the isotopic or isobaric labeling on the modification sites in gel-free methods. Recent advances in MS instrumentation (e.g., Orbitrap Elite) have also greatly improved the sensitivity and accuracy of quantitative proteomic analysis. While these gel-free methods overcome some of the limitations of gel-based methods, they too have limitations. For example, gel-free methods have bias toward high abundance proteins (75). A MSbased shotgun proteomic approach involving a long liquid chromatography separation to improve the resolution may still miss a lot of information. MS-based methods also require the use of highly specialized, expensive equipment, which may not be accessible for all researchers. When comparing gel-based and gel8

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free methods in analyzing the same sample, researchers found that these two approaches provide complementary information, although gel-free MS-based methods usually identified more SNO-proteins than the gel-based approaches (37, 50, 68, 75, 76). Perhaps the best approach for these methods is using them in conjunction with one another, so that complementary information is collected.

INVESTIGATION OF S-NITROSYLATION IN NEURODEGENERATIVE DISEASES As mentioned above, protein S-nitrosylation plays an important role in mediating diverse biological processes under physiological and pathological conditions. In the brain, this redox-based post-translational modification impacts fundamental aspects of neuron and glial functions, such as synaptic plasticity, transcriptional regulation and survival. Dysregulation of S-nitrosylation is implicated in the pathogenesis of neurodegenerative disorders, including stroke, Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS). These diseases are prevalent among our aging population. Molecular mechanisms causing neurodegeneration remain intangible; nevertheless, aberrant S-nitrosylation-induced protein aggregation, endoplasmic reticulum (ER) stress, mitochondrial dysfunction, and synaptic damage, were observed along with neuronal injury (6). Therefore, protein Snitrosylation has been intensively investigated in neurodegenerative diseases by diverse methods. S-Nitrosylation of several critical proteins has been linked to abnormal protein misfolding and aggregation in various neurodegenerative diseases. In human PD and AD brains and sporadic ALS spinal cord tissues, S-nitrosylation level of PDI is significantly increased together with the accumulation of misfolded protein (8, 77). PDI is a member of the ER chaperone enzymes and mediates protein folding during protein synthesis and maturation through the thiol–disulfide exchange mechanism. S-Nitrosylation of PDI on cysteine in the thioredoxin-like domain alters its isomerase activity and compromises its role as a neuroprotective chaperone against protein aggregation. As a result, misfolded proteins accumulate and ER stress dramatically increases, resulting in neuronal cell death in neurodegenerative disorders (8, 77-81). Remarkably, SNO-PDI may be considered as a potential biomarker for the diagnosis of AD, PD, and ALS, since PDI is only aberrantly S-nitrosylated under these conditions (6). Parkin is an E3 ubiquitin ligase that targets damaged and mis-folded proteins to ubiquitin proteasome system for degradation. Excessive levels of NO resulting from environmental neurotoxins such as pesticides and herbicides can induce S-nitrosylation of parkin on the cysteine in the RING I domain. Disruption of E3 ligase activity by this modification results in abnormal protein aggregates and formation of protein inclusion in PD, triggering pathways that lead to neuron loss (12, 36). In addition, S-nitrosylation of parkin up-regulates p53 expression, contributing to p53-mediated cell death and the pathogenesis of PD (82). Another E3 ubiquitin ligase, XIAP inhibits apoptosis and conducts neuroprotection through binding to caspase-3, -7, and -9, reducing their activity, and targeting them for degradation (83, 84). Upon nitrosative stress, formation of SNO-XIAP, predominantly via Cys450 in the RING domain, does not affect its E3 ligase activity but prevents it from inhibiting caspase activity and thus is involved in neuronal cell death (9, 10). Increased levels of SNO-XIAP have been found in brains of human patients with HD, PD and AD. These results suggest that dysregulaiton of S-nitrosylation contributes to neurodegeneration in part through compromising ubiquitin-proteasome pathways. Mitochondria are essential organelles for mediating diverse cellular processes that include ATP production, Ca2+ homeostasis, and generation of free radical species. Neurons critically depend on mitochondria for growth, survival and function. Defects in mitochondrial dynamics and trafficking have been shown to play an important role in the pathogenesis of neurodegenerative disorders. Drp1 is a GTPase associated with mitochondrial fission, and SNO-Drp1 is elevated in human brains suffering from AD and HD (11, 85). In models of AD and HD, S-nitrosylation predominantly on Cys644 hyperactivates the GTPase activity of Drp1, causing excessive mitochondrial fragmentation and decreased synaptic function. Recently, Snitrosylation of cyclin-dependent kinase 5 (Cdk5), an upstream regulator of mitochondrial dynamics, was found to function as a nascent S-nitrosylase for Drp1, suggesting that transnitrosylation of NO from Cdk5 to Drp1 might be involved in SNO-Cdk5–mediated dendritic spine loss (86). Mitochondrial ubiquitin ligase

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(MITOL) is another protein required to maintain mitochondrial functions. It mediates mitochondrial dynamics at least in part through the ubiquitination of Drp1, microtubule-associated protein 1B and mitofusin2 (87). Suppression of MITOL by S-nitrosylation induces the accumulation of microtubuleassociated protein 1B-light chain 1, subsequently resulting in stabilized microtubules, mitochondrial aggregation/dysfunction, and eventually neuronal injury (88). NO is known to regulate transcription by S-nitrosylating transcription factors and other proteins in transcription signaling pathways. MEF2 is a transcription factor that mediates, apoptosis, cell survival and proliferation in multiple organs (89). In the brain, MEF2 regulates transcriptional activity of several target genes (e.g. PGC1α, Bcl-xL, and TLX) associated with neuronal survival and neurogenesis (13, 90). Elevated levels of SNO-MEF2 have been observed in human stroke and AD brains (90). Further investigation revealed that selective S-nitrosylation of Cys39 in the DNA binding domain of MEF2 inhibits its ability to bind the promoter region of downstream genes, and thus perturbs downstream pathways, leading to impaired neurogenesis and neuronal loss in PD, AD, and stroke. Some other proteins, including GAPDH (91-93), DJ-1 (94), PTEN (the phosphatase and tensin homolog) (95), peroxiredoxin 2(96), and matrix metalloproteinase-9 (19), also can be S-nitrosylated impacting various aspects of neuronal function. These SNO-proteins were mostly identified individually by methods including BST and direct MS analysis. To understand mechanisms leading to neuronal degeneration and the impact of protein S-nitrosylation in neurodegenerative diseases in a system view, proteomic tools to measure SNO-proteins and their modification sites have been applied in disease tissues and cellular or animal models; and alterations in S-nitrosylation of many proteins have been demonstrated in different animal models and in specimen from patients. Zahid et al. identified 45 endogenous SNO-proteins from different brain regions of AD patients using three strategies, including modified BST (indirect method), immunoprecipitation and western blotting with antiS-nitrosocysteine antibody (direct methods) (97). Pathway analysis showed that these proteins are mainly associated with metabolism, cytoskeleton, apoptosis, and cell signaling pathways. Using the SNOSID method (33), brain synaptosomal proteins involved in axon guidance and vesicle trafficking were found Snitrosylated only in transgenic mice overexpressing mutated human amyloid precursor protein but not in wild type mice (98). Unfortunately, no quantitative information on protein S-nitrosylation level changes was given in these studies. In an in vivo rat hypoxia-ischemia/reperfusion model, Wiktorowicz et al. utilized the SNOFlo method mentioned above and discovered 35 SNO-proteins that are involved in apoptosis, branching morphogenesis of axons, cortical neurons, and sympathetic neurites, neurogenesis, and calcium signaling (43). With SNORAC coupled with 2-DE, a total of 21 proteins were identified as differentially S-nitrosylated in SH-SY5Y neuroblastoma cells treated with a mitochondrial complex I inhibitor 1-methyl-4 phenylpyridinium (MPP+), which manifests as a cellular model of PD (99). Upregulation of S-nitrosylation levels in esterase D (ESD), serine-threonine kinase receptor-associated protein (STRAP), and T-complex protein 1 subunit γ (TCP-1 γ) and downregulation of that in thioredoxin domain-containing protein 5 precursor (ERp46) responding to the exposure of MPP+ was confirmed by immunoblotting. These studies included quantitative results for SNO-proteins, but SNO-Cys mapping was not able to achieve. Many neurological disorders involve nitrosative stress and proinflammatory responses with activation of microglial cells (100, 101). As discussed above, our investigation using NitroDIGE and ISA in LPSstimulated BV-2 cells has quantitatively profiled a number of SNO-proteins that respond to NO signaling and provided insights into the signaling pathways under microglial activation and shed light on possible molecular mechanisms contributing to neurological diseases. We have also revealed the protective effects of botanical compounds, EGCG from green tea and S-allyl cysteine from aged garlic extract, on nitrosative stress. Besides neurodegenerative diseases, protein S-nitrosylation has been intensively studied in many other diseases, such as cardiovascular diseases, cancer, and immunological diseases. We hope these newly developed quantitative proteomic approaches can be widely applied to investigation of S-nitrosylation in neurodegeneration and other pathophysiological conditions, revealing more critical SNO-proteins and the mechanisms underlying these diseases. Notably, since NOS produces NO locally (102), it is possible that change in local NO concentration significantly alters S-nitrosylation of a target protein impacting its 10

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activities in a subcellular domain, but change in the total SNO-protein level in entire cell is too little to be identified.

CONCLUDING REMARKS Many types of mammalian cells produce NO under normal physiological conditions or enhance nitrosative stress via protein S-nitrosylation. Accumulating evidence demonstrates that aberrant protein S-nitrosylation is involved in a variety of human diseases. The rapid evolution of new methodologies for investigation of protein S-nitrosylation, especially the improvement in quantitative methods, has facilitated in identifying new S-nitrosylation targets, monitoring S-nitrosylation dynamics, and revealing the regulatory role of Snitrosylation in different biological systems. Despite there being over 3000 SNO-proteins reported to date (103), the biological relevance of these proteins remains elusive. False-positive identifications may occur due to absence of a labeling tag on SNOCys, the lack of an appropriate control, or insufficient replicates in experimental design. Additionally, in many reports, high doses of the NO donor (e.g. S-nitrosoglutathione, SNOC), that are out of pathophysiological range, were used to induce S-nitrosylation in protein samples in vitro, resulting in inaccuracy in the S-nitrosylation motif consensus study and prediction of SNO-Cys sites. Recently developed techniques, not only enabled multiplex quantification and unbiased site mapping, but also improved specificity, sensitivity, and accuracy in detecting the modification. These advances make it possible to acquire highly confident data when exploring diverse S-nitrosoproteomes. We anticipate these techniques to aid research in neurodegeneration and many other fields for discovery of disease mechanisms, diagnostic biomarkers, and therapeutic targets for treatment.

ACKNOWLEDGMENTS: We thank Dr. Grace Y. Sun for editorial advice on the manuscript. This publication was made possible by funding of the 5P01ES016738-02 Missouri Consortium from the National Institute of Environmental Health Science (NIEHS) and the Department of Pathology and Anatomical Sciences research fund at University of Missouri (to ZG), as well as by Grant Number P50AT006273 from the National Center for Complementary and Integrative Health (NCCIH), the Office of Dietary Supplements (ODS), and the National Cancer Institute (NCI). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIEHS, NCCIH, ODS, NCI, or the National Institutes of Health.

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1 2 3 4 Table 1. Proteomic approaches for SNO-protein quantification 5 6 7 Methods Reagents Workflow Quantification SNO-Cys site Advantages Capacity mapping 8 9 1. Irreversible labeling by CyDye is resistant to 1. Switch-labeling of SNO-Cys with CyDye reducing reagents 10 2. 2D-DIGE CyDye 2. No SNO-protein enrichment required 11 No S-FLOS (Cy3 and 3. Quantitative analysis of the gel images 2 3. High sensitivity 4. Pick up differentially S-nitrosylated Cy5) 12 4. High signal to noise ratio protein spots for identification with MS 13 14 1. Labeling with the uncharged BD dye does not BODIPY 1. Switch-labeling of SNO-Cys with BD FL2. 2D-DIGE alter the electrophoretic mobility of proteins 15 SNOFlo 2 No maleimide 3. Quantitative analysis of the gel images 2. Quantify S-nitrosylation changes relative to 16 Gel-based (BD) 4. Protein identification with MS analysis protein abundance for improved accuracy 17 methods 1. Pool internal standard from control 18 (fluorescence samples 19 switch) 2. Switch-labeling of SNO-Cys with CyDye 20 Multiple No Pooled internal standard included SNO-DIGE CyDye 3. 2D-DIGE 21 4. Quantitative analysis of the gel images 22 5. Protein identification with MS analysis 23 1. Pool internal standard from all samples 1. Specific and sensitive 24 2. Switch-labeling of SNO-Cys with CyDye 2. Smaller sample size used comparing to BST 25 NitroDIGE CyDye 3. 2D-DIGE Multiple No 3. Internal standard pooled from all samples is 4. Quantitative analysis of the gel images included on each gel for multiplex quantification 26 5. Protein identification with MS analysis with minimized intra-gel variation 27 1. Switch-labeling of SNO-Cys with ICAT 28 ICAT 2. Digestion and anti-biotin affinity 1. Quantify two samples at MS level with good 29 ICAT (light and enrichment 2 Yes accuracy 30 heavy) 3. SNO-Cys identification and 2. Site-specific identification 31 quantification by MS analysis 32 MS-based isotopic 33 Yes, but labeling 34 Isotope- 1. Culture cells in isotope-coded media cannot specify methods 35 coded 2. Cell lysis and processing by BST SNO-Cys in a Labeling protein when culturing cell, so no SILAC 2 or 3 36 peptide that additional labeling step is required lysine and 3. SNO-protein identification and 37 has more than arginine quantification by MS analysis one cysteine 38 39 40 41 42 43 44 12 45 46 ACS Paragon Plus Environment 47 48

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Disadvantages

References

Santhanam et al., 1. Cannot specify SNO-Cys 2. Limited to quantify two samples 2008(39) at a time 1. No site-mapping 2. Gel to gel variation may occur 3. Analyze two samples at a time

Wiktorowicz et al., 2011 (43)

1. No site-mapping 2. The internal standard only includes controls instead of all samples

Chouchani et al., 2010(42)

No site-mapping

Qu et al., 2014(37)

Shift of chromatographic retention Aracenatimes between the light and heavy Parks et al. labeled peptides may cause data 2006(49) misinterpretation 1. Time-consuming for culturing and labeling cells 2. Endogenous biotin contamination may result in Zhou et al., nonspecific signals 2012(60) 3. No tag on SNO-Cys to indicate modification site and discriminate false-positive signals in MS analysis

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1 2 3 4 5 6 7 8 9 10 MS-based 11 isobaric 12 labeling 13 methods 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

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Thioreactive SNO-RAC resin

CysTMT switch labeling

iodoTMT switch labeling

CysTMT sixplex (126-131)

iodoTMT sixplex (126-131)

1. Switch-labeling of SNO-Cys with thiolreactive resin 2. On-resin tryptic digestion and peptide 4 or 8 enrichment 3. Tagging with iTRAQ reagents to identify and quantify SNO-proteins by LC-MS/MS 1. Switch-labeling of SNO-Cys with cysTMT reagents 2. Combine and digest protein samples 3. Anti-TMT enrichment of labeled peptides 6 with acidic elution 4. SNO-Cys identification and quantification by LC-MS/MS analysis 1. Switch-labeling of SNO-Cys by iodoTMT reagents 2. Combine six different samples labeled by sixplex iodoTMT reagents, respectively, and digest 3. Anti-TMT enrichment of labeled peptides 6 with competitive elution 4. LC-MS/MS analysis to locate SNO-Cys and to quantify relative changes in Snitrosylation levels based on reporter ions on iodoTMT reagents

Yes, but not works for peptides Combined labeling and enrichment containing more than one cysteine

Yes

1. SNO-Cys site-mapping 2. Multiplex quantification

Yes

1. Irreversible labeling permitting stable tagging and efficient protein digestion 2. Isobaric multiplex quantification for high through-put and improved accuracy 3. A TMT tag enables immunoaffinity detection, peptide enrichment, SNO-Cys site identification and relative quantification

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1. Reversible labeling 2. The large size of resin may affect its accessibility to SNO-Cys Michael T 3. Re-tagging with iTRAQ for Forrester et quantification requires extra step al., 2009(63) 4. No tag on SNO-Cys during MS analysis to indicate modification site and exclude false-positive signals 1. Reversible labeling resulting in insufficient digestion and low identification efficiency 2. Non-selective peptide elution during enrichment

Christopher I. Murray et al., 2012(67)

Qu et al., 2014(68)

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Table 2. SNO-peptides with MxxC motif identified by ISA in BV-2 microglial cells. Peptide sequence

S-Nitrosylation site

Protein accession

Protein description

Status

iegdmivcaayahelpk

c8

D3YYV8

60S ribosomal protein L5

Known

cpnptcenmnfswr

c6

G3UXT7

RNA-binding protein FUS

Unknown

mlscagadr

c4

I7HLV2

60S ribosomal protein L10

Known

ltdcvvmr

c4

O88569

Heterogeneous nuclear ribonucleoproteins A2/B1

Known, verified in PMID 14722087

mipcdflipvqtqhpir

c4

P06745

Glucose-6-phosphate isomerase

Known

fenlck(imk)*

c5

P07901;P11499

Heat shock protein HSP 90-alpha; Heat shock protein HSP 90-beta

Known, verified in PMID: 15937123

gandfmcdemer

c7

P11983

T-complex protein 1 subunit alpha

Known

(ymek)*cdenilwldyk

c1

P52480

Pyruvate kinase isozymes M1/M2

Known

cpealfqpsflgmescgihettfnsimk

c16

P60710

Actin, cytoplasmic 1

Known, verified in PMID: 18283105

cyemashlr

c1

P62962

Profilin-1

Known, verified in PMID: 18283105

yddmaacmk

c7

P63101

14-3-3 protein zeta/delta

Known, verified in PMID: 24926564

yddmatcmk

c7

P68254

14-3-3 protein theta

Known

ymaccllyr

c5

P68373;P05213;Q3UX10

Known, verified in PMID: 17907787

nmmaacdpr

c6

P99024

Tubulin alpha-1C chain; Tubulin alpha-1B chain; Tubulin alpha chain-like 3 Tubulin beta-5 chain

(dmk)*scqfvavr

c2

Q8CGC7

Bifunctional glutamate/proline--tRNA ligase

Known

qactpmfr

c3

Q9CZN7

Serine hydroxymethyltransferase

Unknown

aaapapeeemdeceqalaaepk

c13

Q9D8N0

Elongation factor 1-gamma

Known

Known, verified in PMID: 17907787

Note: *, sequence in the bracket was added to the peptide identified by ISA in order to show the occurrence of “M” residue on its extension in full protein sequence.

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REFERENCE: 1. Hess, D. T.; Matsumoto, A.; Kim, S. O.; Marshall, H. E.; Stamler, J. S., Protein Snitrosylation: purview and parameters. Nat Rev Mol Cell Biol 2005, 6, (2), 150-66. 2. Greco, T. M.; Hodara, R.; Parastatidis, I.; Heijnen, H. F.; Dennehy, M. K.; Liebler, D. C.; Ischiropoulos, H., Identification of S-nitrosylation motifs by site-specific mapping of the S-nitrosocysteine proteome in human vascular smooth muscle cells. Proc Natl Acad Sci U S A 2006, 103, (19), 7420-5. 3. Seth, D.; Stamler, J. S., The SNO-proteome: causation and classifications. Curr Opin Chem Biol 2011, 15, (1), 129-36. 4. Foster, M. W.; Hess, D. T.; Stamler, J. S., Protein S-nitrosylation in health and disease: a current perspective. Trends Mol Med 2009, 15, (9), 391-404. 5. Gu, Z.; Nakamura, T.; Lipton, S. A., Redox reactions induced by nitrosative stress mediate protein misfolding and mitochondrial dysfunction in neurodegenerative diseases. Mol Neurobiol 2010, 41, (2-3), 55-72. 6. Nakamura, T.; Prikhodko, O. A.; Pirie, E.; Nagar, S.; Akhtar, M. W.; Oh, C. K.; McKercher, S. R.; Ambasudhan, R.; Okamoto, S. I.; Lipton, S. A., Aberrant protein Snitrosylation contributes to the pathophysiology of neurodegenerative diseases. Neurobiol Dis 2015. 7. Hara, M. R.; Thomas, B.; Cascio, M. B.; Bae, B. I.; Hester, L. D.; Dawson, V. L.; Dawson, T. M.; Sawa, A.; Snyder, S. H., Neuroprotection by pharmacologic blockade of the GAPDH death cascade. Proc Natl Acad Sci U S A 2006, 103, (10), 3887-9. 8. Uehara, T.; Nakamura, T.; Yao, D.; Shi, Z. Q.; Gu, Z.; Ma, Y.; Masliah, E.; Nomura, Y.; Lipton, S. A., S-nitrosylated protein-disulphide isomerase links protein misfolding to neurodegeneration. Nature 2006, 441, (7092), 513-7. 9. Tsang, A. H.; Lee, Y. I.; Ko, H. S.; Savitt, J. M.; Pletnikova, O.; Troncoso, J. C.; Dawson, V. L.; Dawson, T. M.; Chung, K. K., S-nitrosylation of XIAP compromises neuronal survival in Parkinson's disease. Proc Natl Acad Sci U S A 2009, 106, (12), 49005. 10. Nakamura, T.; Wang, L.; Wong, C. C.; Scott, F. L.; Eckelman, B. P.; Han, X.; Tzitzilonis, C.; Meng, F.; Gu, Z.; Holland, E. A.; Clemente, A. T.; Okamoto, S.; Salvesen, G. S.; Riek, R.; Yates, J. R., 3rd; Lipton, S. A., Transnitrosylation of XIAP regulates caspase-dependent neuronal cell death. Mol Cell 2010, 39, (2), 184-95. 11. Cho, D. H.; Nakamura, T.; Fang, J.; Cieplak, P.; Godzik, A.; Gu, Z.; Lipton, S. A., S-nitrosylation of Drp1 mediates beta-amyloid-related mitochondrial fission and neuronal injury. Science 2009, 324, (5923), 102-5. 12. Chung, K. K.; Thomas, B.; Li, X.; Pletnikova, O.; Troncoso, J. C.; Marsh, L.; Dawson, V. L.; Dawson, T. M., S-nitrosylation of parkin regulates ubiquitination and compromises parkin's protective function. Science 2004, 304, (5675), 1328-31. 13. Ryan, S. D.; Dolatabadi, N.; Chan, S. F.; Zhang, X.; Akhtar, M. W.; Parker, J.; Soldner, F.; Sunico, C. R.; Nagar, S.; Talantova, M.; Lee, B.; Lopez, K.; Nutter, A.; Shan, B.; Molokanova, E.; Zhang, Y.; Han, X.; Nakamura, T.; Masliah, E.; Yates, J. R., 3rd; Nakanishi, N.; Andreyev, A. Y.; Okamoto, S.; Jaenisch, R.; Ambasudhan, R.; Lipton, S. A., Isogenic human iPSC Parkinson's model shows nitrosative stress-induced dysfunction in MEF2-PGC1alpha transcription. Cell 2013, 155, (6), 1351-64. 14. Stamler, J. S.; Lamas, S.; Fang, F. C., Nitrosylation. the prototypic redox-based signaling mechanism. Cell 2001, 106, (6), 675-83. 15

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15. Lane, P.; Hao, G.; Gross, S. S., S-nitrosylation is emerging as a specific and fundamental posttranslational protein modification: head-to-head comparison with Ophosphorylation. Sci STKE 2001, 2001, (86), re1. 16. Foster, M. W.; McMahon, T. J.; Stamler, J. S., S-nitrosylation in health and disease. Trends Mol Med 2003, 9, (4), 160-8. 17. Jaffrey, S. R.; Erdjument-Bromage, H.; Ferris, C. D.; Tempst, P.; Snyder, S. H., Protein S-nitrosylation: a physiological signal for neuronal nitric oxide. Nat Cell Biol 2001, 3, (2), 193-7. 18. Chen, Y. J.; Ching, W. C.; Lin, Y. P.; Chen, Y. J., Methods for detection and characterization of protein S-nitrosylation. Methods 2013, 62, (2), 138-50. 19. 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, (5584), 1186-90. 20. Choi, Y. B.; Tenneti, L.; Le, D. A.; Ortiz, J.; Bai, G.; Chen, H. S.; Lipton, S. A., Molecular basis of NMDA receptor-coupled ion channel modulation by S-nitrosylation. Nat Neurosci 2000, 3, (1), 15-21. 21. Feelisch, M.; Stamler, J., Methods in nitric oxide research. Wiley-Blackwell: 1996. 22. Cook, J. A.; Kim, S. Y.; Teague, D.; Krishna, M. C.; Pacelli, R.; Mitchell, J. B.; Vodovotz, Y.; Nims, R. W.; Christodoulou, D.; Miles, A. M., Convenient colorimetric and fluorometric assays for S-nitrosothiols. Analytical biochemistry 1996, 238, (2), 150158. 23. Lee, S. J.; Lee, J. R.; Kim, Y. H.; Park, Y. S.; Park, S. I.; Park, H. S.; Kim, K. P., Investigation of tyrosine nitration and nitrosylation of angiotensin II and bovine serum albumin with electrospray ionization mass spectrometry. Rapid Commun Mass Spectrom 2007, 21, (17), 2797-804. 24. Wang, Y.; Liu, T.; Wu, C.; Li, H., A strategy for direct identification of protein Snitrosylation sites by quadrupole time-of-flight mass spectrometry. J Am Soc Mass Spectrom 2008, 19, (9), 1353-60. 25. Mikesh, L. M.; Ueberheide, B.; Chi, A.; Coon, J. J.; Syka, J. E.; Shabanowitz, J.; Hunt, D. F., The utility of ETD mass spectrometry in proteomic analysis. Biochim Biophys Acta 2006, 1764, (12), 1811-22. 26. Riederer, I. M.; Schiffrin, M.; Kovari, E.; Bouras, C.; Riederer, B. M., Ubiquitination and cysteine nitrosylation during aging and Alzheimer's disease. Brain Res Bull 2009, 80, (4-5), 233-41. 27. Faccenda, A.; Bonham, C. A.; Vacratsis, P. O.; Zhang, X.; Mutus, B., Gold nanoparticle enrichment method for identifying S-nitrosylation and S-glutathionylation sites in proteins. J Am Chem Soc 2010, 132, (33), 11392-4. 28. Doulias, P. T.; Greene, J. L.; Greco, T. M.; Tenopoulou, M.; Seeholzer, S. H.; Dunbrack, R. L.; Ischiropoulos, H., Structural profiling of endogenous S-nitrosocysteine residues reveals unique features that accommodate diverse mechanisms for protein Snitrosylation. Proc Natl Acad Sci U S A 2010, 107, (39), 16958-63. 29. Bechtold, E.; Reisz, J. A.; Klomsiri, C.; Tsang, A. W.; Wright, M. W.; Poole, L. B.; Furdui, C. M.; King, S. B., Water-soluble triarylphosphines as biomarkers for protein S-nitrosation. ACS Chem Biol 2010, 5, (4), 405-14.

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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30. Zhang, J.; Li, S.; Zhang, D.; Wang, H.; Whorton, A. R.; Xian, M., Reductive ligation mediated one-step disulfide formation of S-nitrosothiols. Org Lett 2010, 12, (18), 4208-11. 31. Raju, K.; Doulias, P. T.; Evans, P.; Krizman, E. N.; Jackson, J. G.; Horyn, O.; Daikhin, Y.; Nissim, I.; Yudkoff, M.; Nissim, I.; Sharp, K. A.; Robinson, M. B.; Ischiropoulos, H., Regulation of brain glutamate metabolism by nitric oxide and Snitrosylation. Sci Signal 2015, 8, (384), ra68. 32. Forrester, M. T.; Foster, M. W.; Stamler, J. S., Assessment and application of the biotin switch technique for examining protein S-nitrosylation under conditions of pharmacologically induced oxidative stress. J Biol Chem 2007, 282, (19), 13977-83. 33. Hao, G.; Derakhshan, B.; Shi, L.; Campagne, F.; Gross, S. S., SNOSID, a proteomic method for identification of cysteine S-nitrosylation sites in complex protein mixtures. Proc Natl Acad Sci U S A 2006, 103, (4), 1012-7. 34. Chen, Y. J.; Ku, W. C.; Lin, P. Y.; Chou, H. C.; Khoo, K. H.; Chen, Y. J., Salkylating labeling strategy for site-specific identification of the s-nitrosoproteome. J Proteome Res 2010, 9, (12), 6417-39. 35. Huang, B.; Chen, C., Detection of protein S-nitrosation using irreversible biotinylation procedures (IBP). Free Radic Biol Med 2010, 49, (3), 447-56. 36. Yao, D.; Gu, Z.; Nakamura, T.; Shi, Z. Q.; Ma, Y.; Gaston, B.; Palmer, L. A.; Rockenstein, E. M.; Zhang, Z.; Masliah, E.; Uehara, T.; Lipton, S. A., Nitrosative stress linked to sporadic Parkinson's disease: S-nitrosylation of parkin regulates its E3 ubiquitin ligase activity. Proc Natl Acad Sci U S A 2004, 101, (29), 10810-4. 37. Qu, Z.; Meng, F.; Zhou, H.; Li, J.; Wang, Q.; Wei, F.; Cheng, J.; Greenlief, C. M.; Lubahn, D. B.; Sun, G. Y.; Liu, S.; Gu, Z., NitroDIGE analysis reveals inhibition of protein S-nitrosylation by epigallocatechin gallates in lipopolysaccharide-stimulated microglial cells. J Neuroinflammation 2014, 11, 17. 38. Kettenhofen, N. J.; Broniowska, K. A.; Keszler, A.; Zhang, Y.; Hogg, N., Proteomic methods for analysis of S-nitrosation. J Chromatogr B Analyt Technol Biomed Life Sci 2007, 851, (1-2), 152-9. 39. Santhanam, L.; Gucek, M.; Brown, T. R.; Mansharamani, M.; Ryoo, S.; Lemmon, C. A.; Romer, L.; Shoukas, A. A.; Berkowitz, D. E.; Cole, R. N., Selective fluorescent labeling of S-nitrosothiols (S-FLOS): a novel method for studying S-nitrosation. Nitric Oxide 2008, 19, (3), 295-302. 40. Ulrich, C.; Quillici, D. R.; Schegg, K.; Woolsey, R.; Nordmeier, A.; Buxton, I. L., Uterine smooth muscle S-nitrosylproteome in pregnancy. Mol Pharmacol 2012, 81, (2), 143-53. 41. Zhang, H. H.; Feng, L.; Livnat, I.; Hoh, J. K.; Shim, J. Y.; Liao, W. X.; Chen, D. B., Estradiol-17beta stimulates specific receptor and endogenous nitric oxide-dependent dynamic endothelial protein S-nitrosylation: analysis of endothelial nitrosyl-proteome. Endocrinology 2010, 151, (8), 3874-87. 42. Chouchani, E. T.; Hurd, T. R.; Nadtochiy, S. M.; Brookes, P. S.; Fearnley, I. M.; Lilley, K. S.; Smith, R. A.; Murphy, M. P., Identification of S-nitrosated mitochondrial proteins by S-nitrosothiol difference in gel electrophoresis (SNO-DIGE): implications for the regulation of mitochondrial function by reversible S-nitrosation. Biochem J 2010, 430, (1), 49-59.

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

43. Wiktorowicz, J. E.; Stafford, S.; Rea, H.; Urvil, P.; Soman, K.; Kurosky, A.; Perez-Polo, J. R.; Savidge, T. C., Quantification of cysteinyl S-nitrosylation by fluorescence in unbiased proteomic studies. Biochemistry 2011, 50, (25), 5601-14. 44. 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, (4), H1327-35. 45. Huang, B.; Chen, S. C.; Wang, D. L., Shear flow increases S-nitrosylation of proteins in endothelial cells. Cardiovasc Res 2009, 83, (3), 536-46. 46. Gygi, S. P.; Rist, B.; Gerber, S. A.; Turecek, F.; Gelb, M. H.; Aebersold, R., Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol 1999, 17, (10), 994-9. 47. Li, J.; Steen, H.; Gygi, S. P., Protein profiling with cleavable isotope-coded affinity tag (cICAT) reagents: the yeast salinity stress response. Mol Cell Proteomics 2003, 2, (11), 1198-204. 48. Li, C.; Hong, Y.; Tan, Y. X.; Zhou, H.; Ai, J. H.; Li, S. J.; Zhang, L.; Xia, Q. C.; Wu, J. R.; Wang, H. Y.; Zeng, R., Accurate qualitative and quantitative proteomic analysis of clinical hepatocellular carcinoma using laser capture microdissection coupled with isotope-coded affinity tag and two-dimensional liquid chromatography mass spectrometry. Mol Cell Proteomics 2004, 3, (4), 399-409. 49. Aracena-Parks, P.; Goonasekera, S. A.; Gilman, C. P.; Dirksen, R. T.; Hidalgo, C.; Hamilton, S. L., Identification of cysteines involved in S-nitrosylation, Sglutathionylation, and oxidation to disulfides in ryanodine receptor type 1. J Biol Chem 2006, 281, (52), 40354-68. 50. Fu, C.; Hu, J.; Liu, T.; Ago, T.; Sadoshima, J.; Li, H., Quantitative analysis of redox-sensitive proteome with DIGE and ICAT. J Proteome Res 2008, 7, (9), 3789-802. 51. Zhang, X.; Huang, B.; Zhou, X.; Chen, C., Quantitative proteomic analysis of Snitrosated proteins in diabetic mouse liver with ICAT switch method. Protein Cell 2010, 1, (7), 675-87. 52. 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, (11), 2498-509. 53. Fares, A.; Rossignol, M.; Peltier, J. B., Proteomics investigation of endogenous Snitrosylation in Arabidopsis. Biochem Biophys Res Commun 2011, 416, (3-4), 331-6. 54. Paige, J. S.; Xu, G.; Stancevic, B.; Jaffrey, S. R., Nitrosothiol reactivity profiling identifies S-nitrosylated proteins with unexpected stability. Chem Biol 2008, 15, (12), 1307-16. 55. Ong, S. E.; Blagoev, B.; Kratchmarova, I.; Kristensen, D. B.; Steen, H.; Pandey, A.; Mann, M., Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 2002, 1, (5), 37686. 56. Mann, M., Functional and quantitative proteomics using SILAC. Nat Rev Mol Cell Biol 2006, 7, (12), 952-8. 57. Kruger, M.; Moser, M.; Ussar, S.; Thievessen, I.; Luber, C. A.; Forner, F.; Schmidt, S.; Zanivan, S.; Fassler, R.; Mann, M., SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell 2008, 134, (2), 353-64. 18

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Journal of Proteome Research

58. Sury, M. D.; Chen, J. X.; Selbach, M., The SILAC fly allows for accurate protein quantification in vivo. Mol Cell Proteomics 2010, 9, (10), 2173-83. 59. Looso, M.; Borchardt, T.; Kruger, M.; Braun, T., Advanced identification of proteins in uncharacterized proteomes by pulsed in vivo stable isotope labeling-based mass spectrometry. Mol Cell Proteomics 2010, 9, (6), 1157-66. 60. Zhou, X.; Han, P.; Li, J.; Zhang, X.; Huang, B.; Ruan, H. Q.; Chen, C., ESNOQ, proteomic quantification of endogenous S-nitrosation. PLoS One 2010, 5, (4), e10015. 61. Benhar, M.; Thompson, J. W.; Moseley, M. A.; Stamler, J. S., Identification of Snitrosylated targets of thioredoxin using a quantitative proteomic approach. Biochemistry 2010, 49, (32), 6963-9. 62. She, Y. M.; Rosu-Myles, M.; Walrond, L.; Cyr, T. D., Quantification of protein isoforms in mesenchymal stem cells by reductive dimethylation of lysines in intact proteins. Proteomics 2012, 12, (3), 369-79. 63. 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, (6), 557-9. 64. Gauthier, M. A.; Klok, H. A., Peptide/protein-polymer conjugates: synthetic strategies and design concepts. Chem Commun (Camb) 2008, (23), 2591-611. 65. Anand, P.; Hausladen, A.; Wang, Y. J.; Zhang, G. F.; Stomberski, C.; Brunengraber, H.; Hess, D. T.; Stamler, J. S., Identification of S-nitroso-CoA reductases that regulate protein S-nitrosylation. Proc Natl Acad Sci U S A 2014, 111, (52), 18572-7. 66. Hertz, R.; Tovy, A.; Kirschenbaum, M.; Geffen, M.; Nozaki, T.; Adir, N.; Ankri, S., The Entamoeba histolytica Dnmt2 homolog (Ehmeth) confers resistance to nitrosative stress. Eukaryot Cell 2014, 13, (4), 494-503. 67. 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, (2), M111 013441. 68. Qu, Z.; Meng, F.; Bomgarden, R. D.; Viner, R. I.; Li, J.; Rogers, J. C.; Cheng, J.; Greenlief, C. M.; Cui, J.; Lubahn, D. B.; Sun, G. Y.; Gu, Z., Proteomic quantification and site-mapping of S-nitrosylated proteins using isobaric iodoTMT reagents. J Proteome Res 2014, 13, (7), 3200-11. 69. Han, P.; Chen, C., Detergent-free biotin switch combined with liquid chromatography/tandem mass spectrometry in the analysis of S-nitrosylated proteins. Rapid Commun Mass Spectrom 2008, 22, (8), 1137-45. 70. Lee, Y. I.; Giovinazzo, D.; Kang, H. C.; Lee, Y.; Jeong, J. S.; Doulias, P. T.; Xie, Z.; Hu, J.; Ghasemi, M.; Ischiropoulos, H.; Qian, J.; Zhu, H.; Blackshaw, S.; Dawson, V. L.; Dawson, T. M., Protein microarray characterization of the S-nitrosoproteome. Mol Cell Proteomics 2014, 13, (1), 63-72. 71. Stamler, J. S.; Toone, E. J.; Lipton, S. A.; Sucher, N. J., (S)NO signals: translocation, regulation, and a consensus motif. Neuron 1997, 18, (5), 691-6. 72. Marino, S. M.; Gladyshev, V. N., Structural analysis of cysteine S-nitrosylation: a modified acid-based motif and the emerging role of trans-nitrosylation. J Mol Biol 2010, 395, (4), 844-59. 73. Jia, J.; Arif, A.; Terenzi, F.; Willard, B.; Plow, E. F.; Hazen, S. L.; Fox, P. L., Target-selective protein S-nitrosylation by sequence motif recognition. Cell 2014, 159, (3), 623-34. 19

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74. Schwartz, D.; Gygi, S. P., An iterative statistical approach to the identification of protein phosphorylation motifs from large-scale data sets. Nat Biotechnol 2005, 23, (11), 1391-8. 75. Tong, G.; Aponte, A. M.; Kohr, M. J.; Steenbergen, C.; Murphy, E.; Sun, J., Postconditioning leads to an increase in protein S-nitrosylation. Am J Physiol Heart Circ Physiol 2014, 306, (6), H825-32. 76. Scheving, R.; Wittig, I.; Heide, H.; Albuquerque, B.; Steger, M.; Brandt, U.; Tegeder, I., Protein S-nitrosylation and denitrosylation in the mouse spinal cord upon injury of the sciatic nerve. J Proteomics 2012, 75, (13), 3987-4004. 77. Walker, A. K.; Farg, M. A.; Bye, C. R.; McLean, C. A.; Horne, M. K.; Atkin, J. D., Protein disulphide isomerase protects against protein aggregation and is Snitrosylated in amyotrophic lateral sclerosis. Brain 2010, 133, (Pt 1), 105-16. 78. Chen, X.; Guan, T.; Li, C.; Shang, H.; Cui, L.; Li, X. M.; Kong, J., SOD1 aggregation in astrocytes following ischemia/reperfusion injury: a role of NO-mediated S-nitrosylation of protein disulfide isomerase (PDI). J Neuroinflammation 2012, 9, 237. 79. Jeon, G. S.; Nakamura, T.; Lee, J. S.; Choi, W. J.; Ahn, S. W.; Lee, K. W.; Sung, J. J.; Lipton, S. A., Potential effect of S-nitrosylated protein disulfide isomerase on mutant SOD1 aggregation and neuronal cell death in amyotrophic lateral sclerosis. Mol Neurobiol 2014, 49, (2), 796-807. 80. Obukuro, K.; Nobunaga, M.; Takigawa, M.; Morioka, H.; Hisatsune, A.; Isohama, Y.; Shimokawa, H.; Tsutsui, M.; Katsuki, H., Nitric oxide mediates selective degeneration of hypothalamic orexin neurons through dysfunction of protein disulfide isomerase. J Neurosci 2013, 33, (31), 12557-68. 81. Xu, B.; Jin, C. H.; Deng, Y.; Liu, W.; Yang, T. Y.; Feng, S.; Xu, Z. F., Alphasynuclein oligomerization in manganese-induced nerve cell injury in brain slices: a role of NO-mediated S-nitrosylation of protein disulfide isomerase. Mol Neurobiol 2014, 50, (3), 1098-110. 82. Sunico, C. R.; Nakamura, T.; Rockenstein, E.; Mante, M.; Adame, A.; Chan, S. F.; Newmeyer, T. F.; Masliah, E.; Nakanishi, N.; Lipton, S. A., S-Nitrosylation of parkin as a novel regulator of p53-mediated neuronal cell death in sporadic Parkinson's disease. Mol Neurodegener 2013, 8, 29. 83. Holcik, M.; Korneluk, R. G., XIAP, the guardian angel. Nat Rev Mol Cell Biol 2001, 2, (7), 550-6. 84. Salvesen, G. S.; Duckett, C. S., IAP proteins: blocking the road to death's door. Nat Rev Mol Cell Biol 2002, 3, (6), 401-10. 85. Haun, F.; Nakamura, T.; Shiu, A. D.; Cho, D. H.; Tsunemi, T.; Holland, E. A.; La Spada, A. R.; Lipton, S. A., S-nitrosylation of dynamin-related protein 1 mediates mutant huntingtin-induced mitochondrial fragmentation and neuronal injury in Huntington's disease. Antioxid Redox Signal 2013, 19, (11), 1173-84. 86. Qu, J.; Nakamura, T.; Cao, G.; Holland, E. A.; McKercher, S. R.; Lipton, S. A., S-Nitrosylation activates Cdk5 and contributes to synaptic spine loss induced by betaamyloid peptide. Proc Natl Acad Sci U S A 2011, 108, (34), 14330-5. 87. Nagashima, S.; Tokuyama, T.; Yonashiro, R.; Inatome, R.; Yanagi, S., Roles of mitochondrial ubiquitin ligase MITOL/MARCH5 in mitochondrial dynamics and diseases. J Biochem 2014, 155, (5), 273-9.

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88. Yonashiro, R.; Kimijima, Y.; Shimura, T.; Kawaguchi, K.; Fukuda, T.; Inatome, R.; Yanagi, S., Mitochondrial ubiquitin ligase MITOL blocks S-nitrosylated MAP1Blight chain 1-mediated mitochondrial dysfunction and neuronal cell death. Proc Natl Acad Sci U S A 2012, 109, (7), 2382-7. 89. Potthoff, M. J.; Olson, E. N., MEF2: a central regulator of diverse developmental programs. Development 2007, 134, (23), 4131-40. 90. Okamoto, S.; Nakamura, T.; Cieplak, P.; Chan, S. F.; Kalashnikova, E.; Liao, L.; Saleem, S.; Han, X.; Clemente, A.; Nutter, A.; Sances, S.; Brechtel, C.; Haus, D.; Haun, F.; Sanz-Blasco, S.; Huang, X.; Li, H.; Zaremba, J. D.; Cui, J.; Gu, Z.; Nikzad, R.; Harrop, A.; McKercher, S. R.; Godzik, A.; Yates, J. R., 3rd; Lipton, S. A., Snitrosylation-mediated redox transcriptional switch modulates neurogenesis and neuronal cell death. Cell Rep 2014, 8, (1), 217-28. 91. Hara, M. R.; Agrawal, N.; Kim, S. F.; Cascio, M. B.; Fujimuro, M.; Ozeki, Y.; Takahashi, M.; Cheah, J. H.; Tankou, S. K.; Hester, L. D.; Ferris, C. D.; Hayward, S. D.; Snyder, S. H.; Sawa, A., S-nitrosylated GAPDH initiates apoptotic cell death by nuclear translocation following Siah1 binding. Nat Cell Biol 2005, 7, (7), 665-74. 92. Sen, N.; Hara, M. R.; Kornberg, M. D.; Cascio, M. B.; Bae, B. I.; Shahani, N.; Thomas, B.; Dawson, T. M.; Dawson, V. L.; Snyder, S. H.; Sawa, A., Nitric oxideinduced nuclear GAPDH activates p300/CBP and mediates apoptosis. Nat Cell Biol 2008, 10, (7), 866-73. 93. Kornberg, M. D.; Sen, N.; Hara, M. R.; Juluri, K. R.; Nguyen, J. V.; Snowman, A. M.; Law, L.; Hester, L. D.; Snyder, S. H., GAPDH mediates nitrosylation of nuclear proteins. Nat Cell Biol 2010, 12, (11), 1094-100. 94. Ito, G.; Ariga, H.; Nakagawa, Y.; Iwatsubo, T., Roles of distinct cysteine residues in S-nitrosylation and dimerization of DJ-1. Biochem Biophys Res Commun 2006, 339, (2), 667-72. 95. Choi, M. S.; Nakamura, T.; Cho, S. J.; Han, X.; Holland, E. A.; Qu, J.; Petsko, G. A.; Yates, J. R., 3rd; Liddington, R. C.; Lipton, S. A., Transnitrosylation from DJ-1 to PTEN attenuates neuronal cell death in parkinson's disease models. J Neurosci 2014, 34, (45), 15123-31. 96. Fang, J.; Nakamura, T.; Cho, D. H.; Gu, Z.; Lipton, S. A., S-nitrosylation of peroxiredoxin 2 promotes oxidative stress-induced neuronal cell death in Parkinson's disease. Proc Natl Acad Sci U S A 2007, 104, (47), 18742-7. 97. Zahid, S.; Khan, R.; Oellerich, M.; Ahmed, N.; Asif, A. R., Differential Snitrosylation of proteins in Alzheimer's disease. Neuroscience 2014, 256, 126-36. 98. Zareba-Koziol, M.; Szwajda, A.; Dadlez, M.; Wyslouch-Cieszynska, A.; Lalowski, M., Global analysis of S-nitrosylation sites in the wild type (APP) transgenic mouse brain-clues for synaptic pathology. Mol Cell Proteomics 2014, 13, (9), 2288-305. 99. Komatsubara, A. T.; Asano, T.; Tsumoto, H.; Shimizu, K.; Nishiuchi, T.; Yoshizumi, M.; Ozawa, K., Proteomic analysis of S-nitrosylation induced by 1-methyl-4phenylpyridinium (MPP+). Proteome Sci 2012, 10, (1), 74. 100. Perry, V. H.; Holmes, C., Microglial priming in neurodegenerative disease. Nat Rev Neurol 2014, 10, (4), 217-24. 101. Block, M. L.; Hong, J. S., Microglia and inflammation-mediated neurodegeneration: multiple triggers with a common mechanism. Prog Neurobiol 2005, 76, (2), 77-98. 21

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102. Iwakiri, Y.; Satoh, A.; Chatterjee, S.; Toomre, D. K.; Chalouni, C. M.; Fulton, D.; Groszmann, R. J.; Shah, V. H.; Sessa, W. C., Nitric oxide synthase generates nitric oxide locally to regulate compartmentalized protein S-nitrosylation and protein trafficking. Proc Natl Acad Sci U S A 2006, 103, (52), 19777-82. 103. Hess, D. T.; Stamler, J. S., Regulation by S-nitrosylation of protein posttranslational modification. J Biol Chem 2012, 287, (7), 4411-8.

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FIGURE LEGEND Figure 1. Work flows of (A) the representative gel-based (NitroDIGE) and (B) gel-free MS-based (ISA) methods Figure 2. Consensus sequence searching for protein S-nitrosylation (this figure is adopted from Qu et al., 2014, Journal of Proteome Research). The 133 SNO-Cys sites identified from different treatment conditions by ISA in BV-2 microglial cells were analyzed by the Motif-X algorithm, and 6 consensus sequences for protein S-nitrosylation were identified.

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Figure 1. Work flows of (A) the representative gel-based (NitroDIGE) and (B) gel-free MS-based (ISA) methods 142x75mm (300 x 300 DPI)

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Figure 2. Consensus sequence searching for protein S-nitrosylation (this figure is adopted from Qu et al., 2014, Journal of Proteome Research). The 133 SNO-Cys sites identified from different treatment conditions by ISA in BV-2 microglial cells were analyzed by the Motif-X algorithm, and 6 consensus sequences for protein S-nitrosylation were identified. 57x113mm (300 x 300 DPI)

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For TOC Only 331x114mm (300 x 300 DPI)

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