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DOI 10.1002/pmic.201400172

RESEARCH ARTICLE

Analysis of nitrated proteins in Saccharomyces cerevisiae involved in mating signal transduction Jeong Won Kang1∗ , Na Young Lee2∗ , Kyung-Cho Cho1 , Min Young Lee1 , Do-Young Choi1 , Sang-Hyun Park2∗∗ and Kwang Pyo Kim1 1 2

Department of Applied Chemistry, Kyung Hee University, Yongin-si, Gyeonggi-do, Republic of Korea Department of Biological Sciences and Research Center for Functional Cellulomics, Seoul National University, Gwanak-gu, Seoul, Republic of Korea

Protein tyrosine nitration (PTN) is a PTM that regulates signal transduction and inflammatory responses, and is related to neurodegenerative and cardiovascular diseases. The cellular function of PTN remains unclear because the low stoichiometry of PTN limits the identification and quantification of nitrated peptides. Effective enrichment is an important aspect of PTN analysis. In this study, we analyzed the in vivo nitroproteome elicited by mating signal transduction in Saccharomyces cerevisiae using a novel chemical enrichment method followed by LC-MS/MS. Nitroproteome profiling successfully identified changes in the nitration states of 14 proteins during mating signal transduction in S. cerevisiae, making this the first reported in vivo nitroproteome in yeast. We investigated the biological functions of these nitroproteins and their relationships to mating signal transduction in S. cerevisiae using a protein–protein interaction network. Our results suggest that PTN and denitration may be involved in nonreactive nitrogen species-mediated signal transduction and can provide clues for understanding the functional roles of PTN in vivo.

Received: April 28, 2014 Revised: July 17, 2014 Accepted: September 5, 2014

Keywords: Cell biology / LC-MS/MS / Mating signal transduction / Protein tyrosine nitration / Saccharomyces cerevisiae / Yeast

 1

Additional supporting information may be found in the online version of this article at the publisher’s web-site

Introduction

Cells transmit extracellular stimuli to intracellular components through various cellular signaling pathways. The cellular signals are transmitted by PTMs such as phosphorylation, ubiquitination, acetylation, glycosylation, and sumoylation on intracellular proteins [1]. PTMs regulate the activity, stability, interaction, and localization of proteins. The combination of multiple PTMs can increase the strategy diversity of cellular Correspondence: Professor Kwang Pyo Kim, Department of Applied Chemistry, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Republic of Korea E-mail: [email protected] Abbreviations: NO, nitric oxide; PDB, protein data bank; PTN, protein tyrosine nitration; RNS, reactive nitrogen species; SANN, solvent accessibility prediction by nearest neighbor

 C 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

signal transduction and enable elaborate signaling regulation [2]. Therefore, the understanding and identification of PTM mechanisms are crucial for studying signaling networks. Protein tyrosine nitration (PTN) is a PTM that produces 3-nitrotyrosine mediated by reactive nitrogen species such as peroxynitrite (ONOO− ), which is generated by the reaction of nitric oxide (NO) with ROS during oxidative stress [3, 4]. In general, nitric oxide and reactive nitrogen species are produced and accumulated under various cellular conditions [5]. PTN regulates the functional activity, fibrillation, and interactions of proteins in cell differentiation, aging and inflammatory processes, and in several diseases related to these processes [6–9]. These results suggest that PTN plays an important role in cellular signal ∗ These authors contributed ∗∗ Additional corresponding

equally to this work. author: Professor Sang-Hyun Park,

E-mail: [email protected]

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transduction and could be considered as an important biological marker of disease progress [10]. Especially, when exposed to ROS, small molecules such as glutathione, polyamines, flavohemoglobin, and superoxide dismutase are oxidized and remove oxidants in yeast. Moreover, several genes are expressed in response to oxidative stress [11]. In addition, yeast flavohemoglobin, a mitochondrial protein, is involved in the production of NO and mediates hypoxic signaling in yeast [12]. Nevertheless, the mechanism of PTN within in vivo systems remains unclear, because the studies on tyrosine nitration have been focused on a few exemplary proteins related to disease models in mammalian systems [4]. As many signaling pathways are conserved in eukaryotes from yeast to human, yeast could be a good model system for studying the involvement PTN in cellular signaling pathways. Until now PTN in Saccharomyces cerevisiae has been studied by only a few groups. For example, Sahoo et al. researched PTN of catalase as a mechanism for detoxification of peroxynitrite in yeast, and Bhattacharjee et al. identified eight nitroproteins in yeast mitochondria using 2D gel electrophoresis and MALDI MS [13, 14]. In this study, we identified 14 nitroproteins by LC-MS/MS in S. cerevisiae during mating signaling, a well-studied general signaling pathway in yeast. PTN analysis is hampered by the low abundance of 3-nitrotyrosine in vivo, as the biological nitration yield has been reported as approximately 1–5 nitrated residues per 10 000 tyrosine residues [5]. Therefore, there have been many efforts to develop methods to enrich nitrated proteins from various biological samples based on immunochemical enrichment or chemical tagging approaches. Despite their relevance, there has been very little application of such methods to the study of nitrated proteins at the proteome level due to the lack of efficient antibodies and low ionization efficiency of the introduced chemical tags during MS analysis. To resolve this problem, we applied a novel enrichment method that was previously developed using fluorinated carbon tags which were validated by analyzing nitroproteomics [15] and ubiquitin proteomics [16]. Furthermore, we analyzed how signaling pathways and protein function affect biological processes in vivo during mating. Therefore, our analysis of nitroproteins can provide a basis for understanding the functional roles of PTN in S. cerevisiae.

expression vector. The yeast cells were grown in YPD or selective medium at 30⬚C to midlog phase.

2.2 Preparation of S. cerevisiae cellular extracts To analyze changes in the nitroproteome that occurred in response to the mating signal, strain RB200 was grown in YPD at 30⬚C to an A600 of 0.7. To activate the mating pathway, S. cerevisiae cells were treated with 10 ␮M ␣-factor for 15 min, then ␣-factor-treated cells and untreated cells were pelleted by centrifugation at 2000 × g for 5 min at 4⬚C. The cell pellets were washed once with cold water and frozen at −80⬚C. Cells were suspended in lysis buffer (100 mM TrisHCl, pH 7.6, 150 mM NaCl, 0.2% Triton X-100, 1 mM DTT, 2 mM EDTA, protease inhibitor cocktail (Sigma-Aldrich, St. Louis, MO, USA), phosphatase cocktail (Sigma-Aldrich), 1 mM sodium orthovanadate, 1 mM sodium fluoride, 1 mM PMSF) and were lysed with glass beads in a bead beater for 10 min. The soluble fractions were collected by centrifugation at 16 200 × g for 20 min at 4⬚C. The concentration of the lysates was quantified using a Bradford assay kit (Bio-Rad Laboratories, Hercules, CA, USA).

2.3 Tryptic digestion of total cell lysate The total cell lysates (300 ␮g of proteins) from S. cerevisiae were separated using 10% SDS-PAGE, and stained with the GelCode Blue Stain Reagent (Thermo Scientific, Rockford, IL, USA). Each gel lane was sliced into three bands, and the sliced gel was destained and dissolved in 50 mM ammonium bicarbonate (Sigma-Aldrich). Then, aqueous DTT was added to a final concentration of 5 mM and incubated for 45 min at 60⬚C. Additionally, the gel slices were treated with 55 mM iodoacetamide (Sigma-Aldrich) for 30 min at room temperature in darkness for alkylation of cysteine residues. The resulting gel slices were digested by sequencinggrade modified trypsin (Promega, Madison, WI, USA) overnight at 37⬚C. The digested peptides were desalted usR cartridge (Waters Corporation, Milford, MA, ing a Sep-Pak USA).

2.4 Enrichment of nitroproteins using fluorinated carbon tags

2

Materials and methods

2.1 Yeast strains and plasmids construction The S. cerevisiae strain used in this study was RB200 (MATa, trp1, leu2, ura3, his3, can1R, ADE+, mfa2::FUS1-LacZ), which is derived from the wild-type W303–1a strain. For expression of HA-tagged SLA2, the full sequence of SLA2 was synthesized by PCR using yeast genomic DNA as template and was ligated into the MluI-/NotI-digested pRS316-TEF  C 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

The nitrated peptides were selectively enriched via a recently developed method using fluorinated carbon tags [15]. Briefly, digested peptides were acetylated by excess sulfo-NHS-acetate (Thermo Scientific) for inhibition of free amine groups. Acetylated peptides were treated with sodium hydrosulfite (Sigma-Aldrich) to convert 3-nitrotyrosine to 3-aminotyrosine. The reduced peptides were dissolved in 250 mM sodium bicarbonate (pH 8.5) and mixed with 250 mM N-succimidyl-3-perfluorobutyl propionate (Fluorous www.proteomics-journal.com

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Technologies, Pittsburgh, PA, USA). The resulting peptides R silica gel (Fluorous Techwere captured by FluoroFlash nologies) and eluted with 100% methanol after extensive washing with 30% methanol in 10 mM ammonium formate.

2.5 LC-MS/MS analysis Enriched peptides were analyzed using a LTQ-Orbitrap hybrid mass spectrometer equipped with a nanoelectrospray source in positive mode (Thermo Finnigan, San Jose, CA, USA). The enriched peptides were loaded into a trap column (5 ␮m C18 particles into 180 ␮m × 20 mm fused silica capillaries) and separated using a 1.7-␮m BEH analytical column (75 ␮m × 100 mm fused silica capillaries) and a nanoACQUITY ultrapressure LC system (Waters Corporation) at a flow rate of 300 nL/min. The peptides were eluted using solvent A (0.1% formic acid in water (Burdick & Jackson, Muskegon, MI, USA)) and solvent B (0.1% formic acid in acetonitrile (Burdick & Jackson)). The mobile phase gradient of LC was linear from 95% of solvent A to 50% of solvent B for 65 min. The intensity threshold to trigger MS/MS spectrum acquisition was 500, and the normalized collision energy for MS/MS was 35% of the main RF amplitude. Each full MS scan was followed by eight MS/MS scans of the most intense peak to the eighth-most intense peak of the full MS scan in data-dependent mode using the dynamic exclusion options (repeat count was 1, repeat duration was 30 s, and duration was set to 180 s).

2.6 Identification of nitrated peptides To identify nitrated peptides, TurboSEQUEST (Thermo Electron, San Jose, CA, USA) and MS-GF (Generating Function, Center for Computational Mass Spectrometry, San Diego, CA, USA) software [17] were used to search tandem MS spectra against the NCBI reference database for forward sequences (5907 S. cerevisiae protein sequence entries as of January 13, 2013). The tolerance was set to 15 ppm for precursor ions, 1 Da for fragment ions, and one missed trypsin cleavage site was allowed. Oxidation of methionine (+15.99492 Da) and fluorinated carbon tagging of tyrosine (+289.01489 Da) were searched as differential modifications, whereas carbamidomethylation of cysteine (+57.02150 Da) and acetylation of the N-terminus and lysine (+42.01057 Da) were searched as static modifications. For TurboSEQUEST search results, Scaffold (version 4.0.4, Proteome Software Inc., OR, USA) was used to compute protein and peptide probability [18]. Protein and peptide identification were accepted if they had greater than 90% probability. MS-GF search results were validated using the spectral probability cut-off with a Q-value of 0.01. All MS/MS spectra of identified peptides were manually validated.  C 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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2.7 Immunoprecipitation and Western blot An anti-HA agarose (Bethyl Laboratories, Montgomery, TX, USA) was added to 15 mg of cell extracts and incubated with gentle rocking for 4 h at 4⬚C. The agarose beads were washed four times with ice-cold wash buffer (50 mM Tris-Cl, pH 7.5, 150 mM NaCl, 0.1% NP-40, 2 mM EDTA, 1 mM DTT, 1% glycerol) and were resuspended in SDS sample buffer. Proteins were separated by SDS-PAGE and transferred to nitrocellulose transfer membranes. The membranes were blocked with 1% skim milk and BSA in TBST for 1 h at room temperature and were incubated with the anti-HA antibody (Santa Cruz Biotechnology, Dallas, TX, USA) or antinitrotyrosine (Santa Cruz Biotechnology) overnight at 4⬚C. Membranes were washed five times with TBST and incubated with HRP-conjugated antimouse IgG antibody (SigmaAldrich). Proteins were detected by ECL (SuperSignal WestPico Chemiluminescent Substrate, Thermo Scientific) and FUSION-SOLO 2MP (Vilber Luormat, Marne-la-Vall´ee cedex 1, France).

2.8 Comparison of protein expression and nitration levels To quantify nitrated peptides, the ion chromatograms of corresponding MS/MS spectra for each identified nitropeptide were extracted. All extracted MS/MS spectra were validated against identified nitropeptides by their retention time and m/z values. Then, we extracted the peak areas of identified nitropeptides based on their m/z values of precursor ions with the following parameters: peak smoothing by the Gaussian method (point 7) and a mass range tolerance of ± 0.1 Da. All extracted peak areas were normalized to the total area, and the obtained peak area was compared to those of all other samples. In addition, expression levels of identified nitroproteins were compared to the reported expression levels of each protein from previous studies [19, 20].

2.9 Peptide sequences and protein structures analysis To analyze primary sequences, the Two Sample Logo tool was used [21]. We collected amino acid sequences around the identified nitrotyrosine (−7 to +7) as a positive set and samelength sequences around non-nitrotyrosine as a negative set. Two groups of aligned sequences were statistically analyzed by the binomial test (p < 0.01) against a binomial distribution. The JNET algorithm and solvent accessibility prediction by nearest neighbor method (SANN) were used to predict solvent accessibility around nitrotyrosine [22, 23]. We extracted −20 to +20 amino acid residues around nitrotyrosine as the input dataset and performed the search process via the website. Protein secondary structure was predicted by many algorithms such as PSIPRED, JNET, and PROF using the same www.proteomics-journal.com

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transduction were searched in the top 30 functional terms by the order of enrichment score. The functional terms related to mating signal transduction were defined as the terms containing pheromone, mating, conjugation, cell fusion, and actin cytoskeleton organization, including actin cortical patch assembly or localization, actin cortical patch, fungal-type cell wall organization, cellular bud site, spermidine and spermine, sexual reproduction, and sporulation.

2.11 Construction of network model

Figure 1. MS/MS spectrum and immunoblotting analysis of identified nitroproteins in vivo. (A) The workflow of nitroproteome profiling. (B) MS/MS spectrum of SLA2. (#, acetylation of lysine; @, fluorinated carbon tagging of nitrotyrosine; the N-terminus was acetylated). (C) Immunoblotting analysis of nitrotyrosine of SLA2 in cells that were either not treated (lane 1) or treated (lane 2) with 10 ␮M ␣-factor for 15 min. For immunoprecipitation, SLA2 was expressed as an HA-tagged form under the control of the TEF2 promoter.

dataset above [24–28]. The structure of YPD1 (PDB ID 1C03) and MTR4 (PDB ID 2XGJ) were obtained from the protein data bank (PDB) and represented by PyMOL software [29].

2.10 Inference of nitroprotein functions from a functional network To infer the functional roles of the identified nitroproteins in mating signal transduction, we used YeastNet v.3 [30], a probabilistic functional gene network for S. cerevisiae. YeastNet v.3 connects 5805 genes with 362 421 functional links, which represents 99% of all yeast coding genes. By the network-guided guilt-by-association approach, the novel gene functions were estimated with network-connected genes. For a nitroprotein query, all known functional annotations (GO biological process terms) of its interacting genes on YeastNet were collected. Then, the functions related to mating signal  C 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

For network analysis among the identified nitroproteins and mating signal transduction, protein–protein interactions among seed proteins (14 nitroproteins, functionally linked proteins of nitroproteins, and 253 manually curated mating signal transduction proteins) were retrieved from YeastNet v.3. Only protein–protein interactions obtained from small/medium-scale experiments (collected from the protein– protein interaction database) were used for the network construction. The network was clustered into subnetworks by the Markov cluster algorithm [31]. The nodes in the subnetworks not containing nitroproteins were removed. The degree of centrality (K) and shortest-path centrality (SP) were computed using CentiScaPe, and the nonseed proteins with SP = 0 or K = 1 were removed [32]. The nodes with the same GOBPs were grouped into the same modules, each of which was named by the corresponding GOBP. The network was visualized using Cytoscape (v. 2.8.3) [33].

3

Results

3.1 Identification of nitrated proteins from S. cerevisiae in vivo To investigate potential roles of PTN during cellular signaling process in yeast, we chose the yeast mating pathway, because it is well characterized and conserved throughout eukaryotes. To characterize endogenous proteins that are nitrated in response to the mating signal, S. cerevisiae cells were treated with 10 ␮M ␣-factor. Before analysis of nitrated proteins from ␣-factor-treated and -untreated cells by MS, fluorinated carbon tags were attached to nitrated peptides in yeast by the previously reported method [15]. After tagging, the nitrated peptides were isolated exclusively using fluorous solid phase extraction) method as described in the Materials and Methods section, and then analyzed by LC-MS/MS. The list of the identified nitropeptides and corresponding proteins is shown in Table 1. These 14 nitropeptides and corresponding proteins have not been previously reported as PTN targets. The representative spectrum of the nitropeptide corresponding to SLA2 is shown in Fig. 1B. Although there were two tyrosine residues in the nitropeptide from SLA2, “IPLIAESYGIY@K#” (@, nitration of tyrosine; #, acetylation of lysine), the MS/MS spectrum clearly identified that the www.proteomics-journal.com

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Table 1. Nitroproteins in S. cerevisiae

Sample

Symbol

Description

Sequence

Scorea)

Prob.b)

IDc)

Sited)

0 min Both Both

YPD1 INP52 HUL4

ENSIY@LILIAK# PTLQFRPTY@K# FY@SILSNLPTR

0.0044e) 0.0051e) 2.15

0.0054f) 0.0070f) 94.6%

Q07688 P50942 P40985

Y137 Y828 Y204

Both Both Both

ILS1 MTR4 OLE1

Y@FILLESLIK# IEVVEK#DY@VESFR IHHRY@TDTLR

1.72 1.76 1.79

90.0% 95.5% 90.0%

P09436 P47047 P21147

Y254 Y989 Y204

Both 15 min 15 min 15 min

SUR2 CCH1 FBA1 IDP1

VTIDK#Y@K# IVTGY@WK# GAIAAAHY@IR FEQLGIHY@EHR

1.89 1.34 2.58 2.08

90.0% 90.0% 98.7% 96.9%

P38992 P50077 P14540 P21954

Y317 Y745 Y93 Y263

15 min

PHO87

LQIEY@FK#

1.22

90.0%

P25360

Y429

15 min 15 min

SAM3 SLA2

HFY@IK# IPLIAESYGIY@K#

1.39 0.0000e)

90.0% 0.0000f)

Q08986 P33338

Y542 Y198

15 min

YGR237C

Phosphorelay intermediate protein Polyphosphatidylinositol phosphatase HECT domain E3 ubiquitin-protein ligase Isoleucine-tRNA synthetase ATP-dependent RNA helicase Acyl-CoA desaturase 1, oleic acid requiring Sphingolipid C4-hydroxylase Calcium-channel protein Fructose-1,6-bisphosphate aldolase Isocitrate dehydrogenase [NADP], mitochondrial Inorganic phosphate transporter (Involved in activation of PHO pathway) S-adenosylmethionine permease Protein SLA2 (Synthetic lethal with ABP1) Putative protein of unknown function

IQFNNNGIY@K#

0.0073e)

0.0099f)

P50089

Y663

Modifications in the peptide sequences are denoted by #, acetylation at lysine residue; @, fluorinated carbon tag at tyrosine residue; all N-termini were acetylated. a) XCorr value from TurboSEQUEST. b) Peptide probability from Scaffold software. c) Uniprot ID. d) Site of nitrotyrosine. e) Q-value and f) PepQ-value from MS-GF search algorithm.

latter tyrosine residue (Tyr198 ) was nitrated. Moreover, we could not detect any nitropeptides from SLA2 in the control sample. These results suggest that tyrosine nitration occurs on Tyr198 in response to the mating signal. The nitration state of SLA2 protein was confirmed biochemically by immunoblotting using an antinitrotyrosine antibody. The increase of nitrated SLA2 was confirmed in ␣-factor-treated cells compared with a control sample (Fig. 1C), as shown in the MS/MS analysis. The MS/MS spectra of other identified nitropeptides are shown in Supporting Information Fig. 1.

3.2 Selectivity of protein tyrosine nitration Previous studies have proposed that PTMs such as acetylation, phosphorylation, and ubiquitination occur at target residues located in specific sequence motifs, and can be predicted by structural analysis of the reported PTM sites [34–36]. Many computational tools have been developed and used for the analysis of the local environment surrounding PTM sites. These analyses have revealed PTM characteristics and provided information for the prediction of PTMs on target residues. PTN was thought to occur by a nonrandom process even though only a few tyrosine residues were found to be nitrated in vivo [4]. Therefore, it is necessary to investigate factors that may affect PTN such as primary sequence, secondary or tertiary structure, and solvent accessibility to understand the nitration mechanism.  C 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

To investigate the primary sequence characteristics around nitrotyrosine, we extracted amino acid residues surrounding known nitrotyrosine and non-nitrotyrosine (−7 to +7) residues from the 14 identified nitroproteins. As a result, 14 amino acid sequences with nitrated tyrosine residues and 422 other amino acid sequences were collected as the positive and negative sets, respectively. Then, we performed a binomial test (p < 0.01) using the Two Sample Logo tool. Interestingly, most of the enriched amino acid residues around nitrated tyrosine residues were acidic residues, such as aspartic acid, glutamic acid, and histidine, in contrast to unmodified tyrosine residues (Fig. 2A). Previous studies have suggested that basic residues and sulfur-containing residues (e.g., cysteine, methionine) may not interrupt PTN in vivo, and tyrosine residues are nitrated more easily when acidic residues are in close proximity [37, 38]. Our results from yeast nitrated tyrosine sites showed similar patterns to those previously reported. Molecular structure could be an important factor determining the selectivity of target tyrosine residues for nitration, as PTN is a result of direct reaction with peroxynitrite or freely diffusing •NO2 [39]. Tyrosine residues are present in most proteins at an approximately 3.2% abundance, but only a few tyrosine residues are nitrated, depending on their solvent accessibility and molecular structure. To investigate the structural characteristics of nitrotyrosine in yeast, we analyzed protein structures and solvent accessibility. First, we collected amino acid residues around nitrotyrosine from www.proteomics-journal.com

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585 −20 to +20. The JNET algorithm and SANN were used to analyze solvent accessibility of nitroproteins. As shown in Fig. 2B, among the nitrated tyrosine residues, only three tyrosine residues (YPD1, CCH1, and PHO87) were expected to be localized at solvent accessible regions by either one of JNET and SANN prediction algorithms, and seven residues were expected to be localized at the buried region by both algorithms. Detailed analysis of nitroproteins by JNET and SANN are described in Supporting Information Fig. 2. The results of the two algorithms were in agreement that most nitrated tyrosine residues were buried, implying that PTN is a very selective modification and may be accompanied by structural changes. These analyses showed good agreement with our previous report on the in vitro nitration of carbonic anhydrase, indicating that the favorable environment within the buried structure plays a critical role in determining the nitration of the target residues [40]. Finally, we analyzed structures around nitrated tyrosine residues. The secondary structures were predicted by five algorithms, including PSIPRED, JNET, and PROF, and the tertiary structures were analyzed using previously reported structures in the PDB. As shown in Fig. 2C, among 14 predicted nitration sites, eight nitrotyrosines were expected to be localized in a helix structure (Supporting Information Fig. 3). Three tyrosine residues (INP52, OLE1, and SAM3) were expected to be in coiled regions. Three tyrosine residues (IDP1, ILS1, and YGR237C) were predicted to be in strand regions. Although the crystal structures of most identified proteins have not been reported, we could validate the tertiary structures of two proteins, YPD1 and MTR4. In the crystal structures, tyrosine residues were actually located in a helix structure, as predicted (Fig. 2D). Moreover, the nitrotyrosines of YPD1 and MTR4 were exposed and buried, respectively, as predicted by solvent accessibility. 3.3 Relationship between protein tyrosine nitration and the amount of protein in yeast

Figure 2. Sequences and structure analysis of identified nitroproteins from S. cerevisiae. (A) The primary sequence comparison results of nitrated sites and non-nitrated sites around tyrosine residues by the Two Sample Logo tool. The −7 to +7 amino acid sequences in the identified nitroproteins were selected as datasets. Collected datasets were analyzed by the binomial test against a binomial distribution using p-values < 0.01. (B) Graph of predicted solvent accessibility using two algorithms, JNET [22] and SANN [23]. Bar graph and line graph indicate the type of accessibility and significance score of each algorithm, respectively. (C) Predicted secondary structures of nitrotyrosines by PSIPRED [24]. The number of proteins denotes how many identified nitrotyrosines belong to ␣-helices, ␤-strands, and coiled structures. (D) Reported tertiary structures of nitrotyrosine residues in YPD1 and MTR4.

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To verify the correlation between the protein expression levels and nitroproteins, we cataloged all protein expression levels in S. cerevisiae from experimental data of previous studies (Fig. 3A). In previous studies, endogenous protein abundances in S. cerevisiae were quantified using high-affinity tags and fluorescence protein tagging [19, 20]. Interestingly, the peak area of MTR4, which was identified in both samples, was 4–460 times higher than those of the other nitrated peptides. In contrast, IDP1 and PHO87 showed low nitration levels compared to the other nitrated peptides. Some proteins, such as FBA1 and SUR2, were expressed much more than other proteins, but the nitration levels of these proteins were low (Fig. 3C). Some proteins which were barely detected in previous studies, such as CCH1, HUL4, IDP1 or OLE1, were nitrated in this study. Furthermore, we investigated the mRNA expression levels of the identified proteins after mating stimulation (Fig. 3B). There were no significant relationships between nitration and amount of protein expression. www.proteomics-journal.com

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Thus, tyrosine nitration is not proportional to protein expression level and occurs selectively on specific proteins. To investigate whether the nitration process is reversible in yeast, we quantified the nitrated peptides in both conditions by comparing ion intensity chromatograms from MS1 scans of the nitrated peptides. The obtained peak areas were compared to control samples (0 min) and ␣-factor-treated samples (15 min). The quantitative comparison is shown in Fig. 3C. As a result, abundances of tyrosine nitration showed dynamic changes between proteins. Ten nitroproteins (HUL4, SUR2, OLE1, SLA2, FBA1, YGR237C, CCH1, SAM3, IDP1, and PHO87) were highly nitrated in ␣-factor-treated samples, and only one nitroprotein (YPD1) was highly nitrated in control samples. Three other proteins (MTR4, ILS1, and INP52) were nitrated in both samples, but showed no significant change in the abundance of tyrosine nitration. These quantitative changes after ␣-factor treatment indicate that PTN is a reversible PTM in response to altered mating signaling.

3.4 Inference of functions and network analysis of nitroproteins in mating signal transduction pathway

Figure 3. Relationship between PTN and the amount of proteins in S. cerevisiae. (A) Amount of nitrated proteins in vivo from a literature-based search [19, 20]. According to those reports, *, no detectable expression; #, extremely low signal (INP52 and OLE1). (B) Pheromone-induced changes in mRNA expression levels of nitrated proteins from microarray data in previous studies. Each gene expression level was compared between control samples (0 min) and ␣-factor-treated samples (15 min). Several genes showed no expression change after ␣-factor treatment. Genes induced or decreased after ␣-factor treatment are labeled (IDP1, INP52, OLE1, and SUR2). (C) Comparison of nitration level between control and ␣-factor-treated samples. The different shades of gray indicate the nitration level of the identified nitroproteins in each sample. Labeled proteins with * indicate more than twofold increase in nitration levels between two samples.

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To infer the functions of the identified nitroproteins in mating signal transduction, we searched YeastNet v.3 (Table 2). In the YeastNet analysis, the functions related to mating signal transduction were estimated by the order of enrichment scores. According to inference of functions, some proteins seemed to function in mating signal transduction indirectly. For example, ILS1 and OLE1 were active in chromatin silencing at the silent mating-type cassette, and INP52 and SLA2 were related to the actin cortical patch assembly or localization in the mating projection tip, which was their reported cellular localization. Moreover, YPD1 was involved in negative regulation, such as transcription from RNA polymerase II promoters by pheromones and conjugation with cellular fusion. In addition, six nitroproteins (CCH1, FBA1, IDP1, INP52, SLA2, and SUR2) were related to actin cytoskeleton organization, and four nitroproteins (CCH1, FBA1, IDP1, and OLE1) were involved in fungal-type cell wall organization. SAM3 and SUR2 were related to spermidine transport, which promotes mating and fertilization [41]. Based on the functional inference of nitroproteins, we then generated a biological network model to delineate relationships among the 14 nitroproteins in the mating signal transduction pathway, as shown in Fig. 4. The network was constructed by the direct protein–protein interactions between the seed proteins (14 nitroproteins, functional nitroprotein binders, and 253 manually curated mating signal transduction proteins). The modules composed of nitroproteins and their functional interactors were densely connected to the canonical pheromone signal transduction pathway and the cellular functions related to mating process, such as mating type projection tip, actin cortical patch, and actin cytoskeleton organization. MTR4 directly interacted with STE4 www.proteomics-journal.com

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Proteomics 2015, 15, 580–590 Table 2. Predicted GO terms of identified nitroproteins related to mating signal transduction.

Symbol

GO terms

Scorea)

CCH1

Fungal-type cell wall organization Actin filament organization Fungal-type cell wall organization Actin filament organization Fungal-type cell wall organization Actin cytoskeleton organization Actin cortical patch localization Actin cytoskeleton organization Actin cortical patch assembly Actin cytoskeleton organization Chromatin silencing at silent mating-type cassette Fungal-type cell wall organization Chromatin silencing at silent mating-type cassette Spermidine transport Spermine transport Spermidine transport Actin cytoskeleton organization Negative regulation of transcription from RNA-polymerase II promoter by pheromones Negative regulation of conjugation with cellular fusions Null Null Null Null

30.73 11.44 21.42 14.21 6.98 5.91 25.21 14.11 34.40 30.62 14.32 13.78 11.94 12.59 6.69 9.58 8.46 10.22

FBA1 IDP1 INP52 SLA2 ILS1 OLE1 SAM3 SUR2 YPD1

HUL4 MTR4 PHO87 YGR237C

4.62 NaN NaN NaN NaN

Predicted GO annotation of identified nitroproteins related to mating signal transduction in YeastNet v.3. GO annotation showed that all nitroproteins had several functions. Listed are the two highest-scoring mating signaling-related function for each nitroprotein. a) Score from YeastNet v.3.

which is a critical component in canonical pheromone signal transduction. In addition, MTR4 was nitrated at the same residue in control and ␣-factor-treated samples, and was not differentially regulated by ␣-factor. YPD1 was also involved in pheromone signaling, however, it was related to negative regulation of conjugation with cellular fusion as shown in Table 2, and was identified only in control samples. SLA2 and INP52 showed strong interactions with proteins involved in actin cytoskeleton organization, which occurs during cell fusion. YGR237C interacted with GLC7, which was located in the mating projection tip, and the nitration level was increased upon pheromone stimulation. Moreover, SAM3 and SUR2 were involved in spermidine transport, and ILS1 and OLE1 were related to chromatin silencing. However, four nitroproteins, CCH1, HUL4, IDP1, and PHO87 with a few interacting partners were excluded from the network model and these nitroproteins may function in other pathways regulated by mating signal transduction.

4

Discussion

In the present study, we identified 14 nitrated proteins in S. cerevisiae with a nitrotyrosine enrichment method and LC-MS/MS analysis. Upon mating pheromone stimulation, the number of nitrated proteins increased, and most of these proteins were located in the membrane and mitochon C 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

dria. Although oxidative stress by antioxidants activates the pheromone-responsive gene Fus1 via Kss1 phosphorylation [42], there has been no report suggesting that mating stimulation induces nitrogen species in the cytoplasm or tyrosine nitration. This study shows that PTN is increased in a mating stimulation-dependent manner and suggests the possibility that PTN contributes signaling regulation. PTN occurs on the ortho position of tyrosine, so it could compete with tyrosine phosphorylation. We searched tyrosine phosphorylation sites for comparison with the location of nitration residues. Only two tyrosine residues were found to be phosphorylated in FBA1, pTyr294 , and pTyr310 [43, 44], and there were no matching locations between phosphotyrosine and nitrotyrosine residues. Tyrosine phosphorylation is extremely rare, 0.027%, in S. cerevisiae [45]. Therefore, there are limitations to comparing phosphotyrosine and nitrotyrosine. Our analysis of primary sequences and protein structures shows that charged amino acids (e.g., lysine, arginine, glutamic acid, and aspartic acid) were enriched around nitrotyrosines and that more than half of nitrotyrosines were buried. Moreover, their locations are consistent with tertiary structure, as shown in Fig. 2D. Although we performed sequence alignment to obtain a consensus sequence of PTN in yeast, the small number of nitration sites made it difficult to analyze. Nevertheless, we expect that PTN occurs via selective processes.

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Figure 4. Inference of functions and network analysis of nitroproteins in mating signal transduction. The network model was constructed with 14 nitroproteins involved in mating signal transduction using functional interactors of the nitroproteins and manually curated mating signal transduction proteins. Edges denote protein-protein interaction.

In addition, network analysis revealed that modules composed of nitroproteins and their functional interactors were densely connected to the canonical pheromone signal transduction pathway and cellular functions related to the mating process. Interestingly, YPD1, which was nitrated in only the control sample, is involved in negative regulation of conjugation with cellular fusion and osmosensory signaling pathways. YPD1 directly interacted with HOG1, which is the representative protein in the osmotic stress pathway that inhibits pheromone signal transduction [46]. Therefore, these results imply that YPD1 may be regulated by nitration states due to pheromone stimulation and PTN may regulate cross-talk between mating signal transduction. The other nitroproteins involved in those pathways interact with proteins of the canonical pheromone signal transduction pathway. These results suggest that PTN may regulate mating signal transduction by modifying multiple pathway components in a coordinated manner. Most of the nitroproteins identified in our study were not directly related to mating signaling, but were instead indirectly involved in mating signaling. For example, CCH1 is a voltage-gated calcium channel that responds to mating pheromones. When yeast cells mate, calcium influx increases and calcium signaling is activated. The high-affinity Ca2+ influx system, which includes CCH1-MID1, and the lowaffinity Ca2+ influx system are regulated during mating  C 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Proteomics 2015, 15, 580–590

signaling [47]. After activation of the mating pathway, morphological changes in yeast cells occur. INP52 and SLA2 are localized to the mating projection tip, called a shmoo [48]. These proteins are required for cellular morphogenesis and cell wall organization. However, how these proteins are regulated remains unclear. In our study, we found that CCH1 and SLA2 were nitrated on Tyr745 and Tyr198 , respectively, in ␣-factor-treated cells, and INP52 was nitrated on Tyr828 in ␣-factor-treated cells and control cells (Table 1). Human AP180, the human homolog of SLA2, in the human pituitary is nitrated on Tyr237 , which is conserved in SLA2 Tyr198 [49]. These results suggest that PTN may be involved in protein activity regulation or localization during mating signaling. Although PTN occurs on key regulators in signal transduction, how PTN affects regulatory proteins and at which steps PTN is regulated have not been well studied. Unexpectedly, none of the key regulatory proteins involved in mating signaling were identified in this study. It is supposed that (i) PTN does not affect these proteins, (ii) the amount of endogenous protein or nitrated protein is too low to analyze using mass analysis, or (iii) PTN is a transient event on key components in vivo. In a previous global analysis of the phosphoproteome in yeast [50, 51], phosphorylations of key components such as STE5, STE11, STE7, FUS3, or KSS1 were not detected by MS, even though those sites were well established in mutant studies or confirmed by specific phosphoantibodies. Therefore, it is still possible that PTN is involved in protein regulation and signaling. In further studies, the function of PTN in yeast nitrated proteins should be investigated. It could be interesting to test whether impaired PTN affects protein activity, localization or interactions. All integrated data could provide detailed information regarding the PTN mechanism. This work was financially supported by the Proteogenomic Research Program and the Bio and Medical Technology Development Program (Project No. 2012M3A9B6055305) through the National Research Foundation of Korea funded by the Korean Ministry of Science, ICT & Future Planning (to K.P.K.), and also supported by grants from the National Research Foundation of Korea (NRF-2012R1A1A2009248 and NRF-2007-0055514) (to S.-H.P.). J.W.K. was supported by TJ Park Science Fellowship funded by POSCO TJ Park Foundation. The authors have declared no conflict of interest.

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References

[1] Mann, M., Jensen, O. N., Proteomic analysis of posttranslational modifications. Nat. Biotechnol. 2003, 21, 255– 261. [2] Deribe, Y. L., Pawson, T., Dikic, I., Post-translational modifications in signal integration. Nat. Struct. Mol. Biol. 2010, 17, 666–672. [3] Yeo, W. S., Lee, S. J., Lee, J. R., Kim, K. P., Nitrosative protein tyrosine modifications: biochemistry and functional significance. BMB Rep. 2008, 41, 194–203.

www.proteomics-journal.com

Proteomics 2015, 15, 580–590 [4] Abello, N., Kerstjens, H. A., Postma, D. S., Bischoff, R., Protein tyrosine nitration: selectivity, physicochemical and biological consequences, denitration, and proteomics methods for the identification of tyrosine-nitrated proteins. J. Proteome Res. 2009, 8, 3222–3238. [5] Radi, R., Nitric oxide, oxidants, and protein tyrosine nitration. Proc. Natl. Acad. Sci. USA 2004, 101, 4003–4008. [6] Giasson, B. I., Duda, J. E., Murray, I. V., Chen, Q. et al., Oxidative damage linked to neurodegeneration by selective alphasynuclein nitration in synucleinopathy lesions. Science 2000, 290, 985–989. [7] Cappelletti, G., Maggioni, M. G., Tedeschi, G., Maci, R., Protein tyrosine nitration is triggered by nerve growth factor during neuronal differentiation of PC12 cells. Exp. Cell Res. 2003, 288, 9–20. [8] Lee, J. R., Kim, J. K., Lee, S. J., Kim, K. P., Role of protein tyrosine nitration in neurodegenerative diseases and atherosclerosis. Arch. Pharm. Res. 2009, 32, 1109–1118. [9] Yakovlev, V. A., Bayden, A. S., Graves, P. R., Kellogg, G. E., Mikkelsen, R. B., Nitration of the tumor suppressor protein p53 at tyrosine 327 promotes p53 oligomerization and activation. Biochemistry 2010, 49, 5331–5339. [10] Souza, J. M., Peluffo, G., Radi, R., Protein tyrosine nitration– functional alteration or just a biomarker? Free Radic. Biol. Med. 2008, 45, 357–366. [11] Jamieson, D. J., Oxidative stress responses of the yeast Saccharomyces cerevisiae. Yeast 1998, 14, 1511–1527. [12] Liu, L., Zeng, M., Hausladen, A., Heitman, J., Stamler, J. S., Protection from nitrosative stress by yeast flavohemoglobin. Proc. Natl. Acad. Sci. USA 2000, 97, 4672–4676. [13] Sahoo, R., Bhattacharjee, A., Majumdar, U., Ray, S. S. et al., A novel role of catalase in detoxification of peroxynitrite in S. cerevisiae. Biochem. Biophys. Res. Commun. 2009, 385, 507–511. [14] Bhattacharjee, A., Majumdar, U., Maity, D., Sarkar, T. S. et al., In vivo protein tyrosine nitration in S. cerevisiae: identification of tyrosine-nitrated proteins in mitochondria. Biochem. Biophys. Res. Commun. 2009, 388, 612–617. [15] Kim, J. K., Lee, J. R., Kang, J. W., Lee, S. J. et al., Selective enrichment and mass spectrometric identification of nitrated peptides using fluorinated carbon tags. Anal. Chem. 2011, 83, 157–163. [16] Brittain, S. M., Ficarro, S. B., Brock, A., Peters, E. C., Enrichment and analysis of peptide subsets using fluorous affinity tags and mass spectrometry. Nat. Biotechnol. 2005, 23, 463– 468. [17] Kim, S., Gupta, N., Pevzner, P. A., Spectral probabilities and generating functions of tandem mass spectra: a strike against decoy databases. J. Proteome Res. 2008, 7, 3354– 3363. [18] Searle, B. C., Scaffold: a bioinformatic tool for validating MS/MS-based proteomic studies. Proteomics 2010, 10, 1265–1269. [19] Roberts, C. J., Nelson, B., Marton, M. J., Stoughton, R. et al., Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles. Science 2000, 287, 873–880.

 C 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

589 [20] Ghaemmaghami, S., Huh, W. K., Bower, K., Howson, R. W. et al., Global analysis of protein expression in yeast. Nature 2003, 425, 737–741. [21] Vacic, V., Iakoucheva, L. M., Radivojac, P., Two Sample Logo: a graphical representation of the differences between two sets of sequence alignments. Bioinformatics 2006, 22, 1536– 1537. [22] Cole, C., Barber, J. D., Barton, G. J., The Jpred 3 secondary structure prediction server. Nucleic Acids Res. 2008, 36, W197–201. [23] Joo, K., Lee, S. J., Lee, J., Sann: solvent accessibility prediction of proteins by nearest neighbor method. Proteins 2012, 80, 1791–1797. [24] Jones, D. T., Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 1999, 292, 195–202. [25] McGuffin, L. J., Bryson, K., Jones, D. T., The PSIPRED protein structure prediction server. Bioinformatics 2000, 16, 404– 405. [26] Cuff, J. A., Barton, G. J., Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins 2000, 40, 502–511. [27] Ouali, M., King, R. D., Cascaded multiple classifiers for secondary structure prediction. Protein Sci. 2000, 9, 1162–1176. [28] Rost, B., Review: protein secondary structure prediction continues to rise. J. Struct. Biol. 2001, 134, 204–218. [29] DeLano, W. L., The PyMOL Molecular Graphics System. DeLano Scientific, 2002, San Carlos, CA, USA. [30] Kim, H., Shin, J., Kim, E., Hwang, S. et al., YeastNet v3: a public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae. Nucleic Acids Res. 2014, 42, D731–736. [31] Enright, A. J., Van Dongen, S., Ouzounis, C. A., An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 2002, 30, 1575–1584. [32] Scardoni, G., Petterlini, M., Laudanna, C., Analyzing biological network parameters with CentiScaPe. Bioinformatics 2009, 25, 2857–2859. [33] Smoot, M. E., Ono, K., Ruscheinski, J., Wang, P. L., Ideker, T., Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 2011, 27, 431–432. [34] Basu, A., Rose, K. L., Zhang, J., Beavis, R. C. et al., Proteomewide prediction of acetylation substrates. Proc. Natl. Acad. Sci. USA 2009, 106, 13785–13790. [35] Ritz, A., Shakhnarovich, G., Salomon, A. R., Raphael, B. J., Discovery of phosphorylation motif mixtures in phosphoproteomics data. Bioinformatics 2009, 25, 14–21. [36] Radivojac, P., Vacic, V., Haynes, C., Cocklin, R. R. et al., Identification, analysis, and prediction of protein ubiquitination sites. Proteins 2010, 78, 365–380. [37] Ischiropoulos, H., Biological selectivity and functional aspects of protein tyrosine nitration. Biochem. Biophys. Res. Commun. 2003, 305, 776–783. [38] Sacksteder, C. A., Qian, W. J., Knyushko, T. V., Wang, H. et al., Endogenously nitrated proteins in mouse brain: links to neurodegenerative disease. Biochemistry 2006, 45, 8009–8022.

www.proteomics-journal.com

590

J. W. Kang et al.

Proteomics 2015, 15, 580–590 cerevisiae by electron transfer dissociation (ETD) mass spectrometry. Proc. Natl. Acad. Sci. USA 2007, 104, 2193–2198.

[39] Surmeli, N. B., Litterman, N. K., Miller, A. F., Groves, J. T., Peroxynitrite mediates active site tyrosine nitration in manganese superoxide dismutase. Evidence of a role for the carbonate radical anion. J. Am. Chem. Soc. 2010, 132, 17174– 17185.

[46] Nagiec, M. J., Dohlman, H. G., Checkpoints in a yeast differentiation pathway coordinate signaling during hyperosmotic stress. PLoS Genet. 2012, 8, e1002437.

[40] Lee, S. J., Kang, J. W., Cho, K. C., Kabir, M. M. et al., Mass Spectrometry Analysis of In Vitro Nitration of Carbonic Anhydrase II. Bull. Korean Chem. Soc. 2014, 35, 709– 714.

[47] Muller, E. M., Locke, E. G., Cunningham, K. W., Differential regulation of two Ca(2+) influx systems by pheromone signaling in Saccharomyces cerevisiae. Genetics 2001, 159, 1527–1538.

[41] Bauer, M. A., Carmona-Gutierrez, D., Ruckenstuhl, C., Reisenbichler, A. et al., Spermidine promotes mating and fertilization efficiency in model organisms. Cell Cycle 2013, 12, 346– 352.

[48] Narayanaswamy, R., Moradi, E. K., Niu, W., Hart, G. T. et al., Systematic definition of protein constituents along the major polarization axis reveals an adaptive reuse of the polarization machinery in pheromone-treated budding yeast. J. Proteome Res. 2009, 8, 6–19.

[42] Staleva, L., Hall, A., Orlow, S. J., Oxidative stress activates FUS1 and RLM1 transcription in the yeast Saccharomyces cerevisiae in an oxidant-dependent Manner. Mol. Biol. Cell 2004, 15, 5574–5582. [43] Li, X., Gerber, S. A., Rudner, A. D., Beausoleil, S. A. et al., Large-scale phosphorylation analysis of alpha-factorarrested Saccharomyces cerevisiae. J. Proteome Res. 2007, 6, 1190–1197. [44] Albuquerque, C. P., Smolka, M. B., Payne, S. H., Bafna, V. et al., A multidimensional chromatography technology for in-depth phosphoproteome analysis. Mol. Cell. Proteomics 2008, 7, 1389–1396. [45] Chi, A., Huttenhower, C., Geer, L. Y., Coon, J. J. et al., Analysis of phosphorylation sites on proteins from Saccharomyces

 C 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

[49] Zhan, X., Desiderio, D. M., The human pituitary nitroproteome: detection of nitrotyrosyl-proteins with twodimensional Western blotting, and amino acid sequence determination with mass spectrometry. Biochemical Biophys. Res. Commun. 2004, 325, 1180–1186. [50] Ficarro, S. B., McCleland, M. L., Stukenberg, P. T., Burke, D. J. et al., Phosphoproteome analysis by mass spectrometry and its application to Saccharomyces cerevisiae. Nat. Biotechnol. 2002, 20, 301–305. [51] Gruhler, A., Olsen, J. V., Mohammed, S., Mortensen, P. et al., Quantitative phosphoproteomics applied to the yeast pheromone signaling pathway. Mol. Cell. Proteomics 2005, 4, 310–327.

www.proteomics-journal.com