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. Author manuscript; available in PMC: 2010 Nov 22.
Published in final edited form as: Proteomics Clin Appl. 2009 Nov 1;3(11):1288–1295. doi: 10.1002/prca.200900005

Quantitative Proteomic Analysis of Ovarian Cancer Cells Identified Mitochondrial Proteins Associated with Paclitaxel Resistance

Yuan Tian 1, Aik-Choon Tan 2, Xiaer Sun 1, Matthew T Olson 1, Zhi Xie 3, Natini Jinawath 1, Daniel W Chan 1, Ie-Ming Shih 1, Zhen Zhang 1, Hui Zhang 1,*
PMCID: PMC2989613  NIHMSID: NIHMS247411  PMID: 21113235

Abstract

Paclitaxel has been widely used as an anti-mitotic agent in chemotherapy for a variety of cancers and adds substantial efficacy as the first-line chemotherapeutic regimen for ovarian cancers. However, the frequent occurrence of paclitaxel resistance limits its function in long-term management. Despite abundant clinical and cellular demonstration of paclitaxel resistant tumors, the molecular mechanisms leading to paclitaxel resistance are poorly understood. Using genomic approaches, we have previously identified an association between a BTB/POZ gene, Nac1, and paclitaxel resistance in ovarian cancer. The experiments presented here have applied multiple quantitative proteomic methods to identify protein changes associated with paclitaxel resistance and Nac1 function. The SKOV-3 ovarian serous carcinoma cell line, which has inducible expression of dominant negative Nac1, was used to determine the paclitaxel treatment associated changes in the presence and absence of functional Nac1. Quantitative proteomic analyses were performed using iTRAQ labeling and mass spectrometry. Two label-free quantitative proteomic methods: LC-MS and spectral count were used to increase confidence of proteomic quantification. A total of 1371 proteins were quantified by at least one of the quantitative proteomic methods. Candidate proteins related to paclitaxel and NAC1 function were identified in this study. Go analysis of the protein changes identified upon paclitaxel resistance revealed that cell component enrichment related to mitochondria. Moreover, tubulin and mitochondrial proteins were the major cellular components with changes associated with paclitaxel treatment. This suggests that mitochondria may play a role in paclitaxel resistance.

Keywords: ovarian cancer, paclitaxel, Taxol, mass spectrometry, proteomics

1. Introduction

Paclitaxel (Taxol®) is a potent antimitotic agent which is currently employed for the treatment of many human cancers and as an inflammation deterrent in drug-eluting cardiovascular stents [1]. Paclitaxel is known to induce cytotoxicity by preventing tubulin depolymerization during the metaphase to anaphase transition of mitosis [2, 3] or by triggering apoptosis through regulating the expression of apoptosis-related proteins in both the caspase-dependent and caspase-independent pathways [1]. Unfortunately, while paclitaxel causes initial remission of ovarian cancer, the tumor often acquires resistance and recurs [4]. The molecular mechanisms underlying paclitaxel resistance remain unclear. The experiments presented here attempt to identify the proteins associated with paclitaxel resistance in ovarian cancer cells in order to facilitate the elucidation of molecular mechanisms of paclitaxel induced apoptosis and acquired resistance and discovery of potential drug targets for ovarian cancer with paclitaxel resistance.

Nac1, a member of BTB/POZ gene family, is a transcription repressor that is essential for the growth and survival of tumor cells [5]. We have previously associated Nac1 overexpression with tumor recurrence and paclitaxel resistance in ovarian serous carcinoma [5, 6]. However, the function of Nac1 for paclitaxel resistance is not well understood. To explore this function, we generated the SKOV-3 N130 cell line which is an ovarian serous carcinoma cell line (SKOV-3) with stable transfection of N130/EGFP controlled by tTA (tetracycline-controlled transactivator) [5]. This Tet-OFF inducible system can trigger the expression of N130 by removal of doxycycline which inhibits the function of Nac1.

The relationships between paclitaxel resistance and Nac1 were explored here using iTRAQ (isoabaric tags for relative and absolute quantitation) quantitation method, and also measured by label-free quantitation methods, LC-MS and spectral count. The three methods are among the several high throughput quantitative proteomic methods that have been developed in the past decade. Method development in this field has occurred in two directions: label-dependent and label-free. The label-dependent methods are widely used and include derivitizing methods [7] such as isotope-coded affinity tags (ICAT) [8] and isobaric tags for relative and absolute quantitation (iTRAQ) [9] and non-derivitizing methods such as stable isotope labeling with amino acids in cell culture (SILAC) [10] and 18O labeling [11]. When applied to proteomics, stable isotope labeling allows for the accurate measurement of the relative peptide abundance by direct comparison of light and heavy peptides in the same spectrum.

While label-dependent methods comprise the gold standard for quantitative techniques, only a limited number of samples can be quantified using isotopic derivitization in a single experiment due to the fixed number of channels from the labeling reagents. Thus an alternative direction of method development for quantitative proteomics, that of label-free quantitation methods, has evolved and includes the liquid chromatography-mass spectrometry (LC-MS) method [12, 13] and spectral count [14]. The LC-MS method determines the peptide abundance by comparing the intensity of the same peptide peak in multiple LC-MS runs. Quantitation of protein abundance by spectral count is based on the number of redundant spectra acquired for each protein from different samples in the LC-MS/MS analyses. Label-free quantitative methods are theoretically capable of quantifying an unlimited number of samples in a single study. The limitation of the label-free quantitative method is that the quantitation accuracy relies heavily on the reproducible analyses of different samples in multiple LC-MS and LC-MS/MS analyses [1518].

Therefore, it is clear that a high-throughput quantitative proteomic method has the potential to identify a large number of protein changes but that these measurements must be validated. The validation of these protein changes with traditional methods such as Western blots or immunohistochemistry is limited due to the availability, expense of the antibodies, and the low throughput of the assays. Therefore, the proteomics study should give the more confident ones for the further immune based validation. The study presented here identified protein changes related to paclitaxel resistance and Nac1 function using most reliable quantitation, iTRAQ labeling. In addition, the two label-free quantitative proteomic methods were used to increase the quantification confidence.

Using the iTRAQ quantitation, most of these changed proteins related to paclitaxel treatment were significantly overrepresented in mitochondria. Our results suggest a new role of mitochondria of ovarian cancer cells in paclitaxel resistance and define potential new targets for treatment of paclitaxel-resistant ovarian cancer. Most protein changes in mitochondria were also identified as up-regulated by the two label-free methods. In addition, we also list the candidate proteins related to NAC1 function.

2 Materials and methods

2.1 Materials

Sequencing grade trypsin was from Promega (Madison, WI); C18 Sep-Pak Vac columns were from Waters (Milford, MA); α-cyano-4-hydroxycinnasmic acid (CHCA) was from Agilent (Palo Alto, CA); iTRAQ reagent and mass calibration standards were from Applied Biosystems (Foster City, CA); BCA assay kit was from Pierce (Rockford, IL); SCX columns and C18 resin were from Sepax (Newark, DE).; Other chemicals were purchased from Sigma-Aldrich (St. Louis, MO).

2.2 Treatments of ovarian cancer cells

The N130-inducible SKOV-3 ovarian cancer cell line, stably expressed the inducible construct of N-terminal 130 amino acids (N130) of Nac1 gene and enhanced green fluorescent protein (EGFP), was generated and reported previously [5, 6]. N130-inducible SKOV-3 cells were cultured in G400D2 medium (RPMI medium contained 10% FBS, 1% penicillin/streptomycin, 400 μg/ml geneticin, and 2 μg/ml doxycycline). To induce the expression of N130, the cells were washed with PBS twice and cultured in G400 medium (G400D2 medium without doxycycline). Expression of N130-EGFP was confirmed by fluorescent microscope after 28-hour culture in G400 medium.

The N130-inducible SKOV-3 ovarian adenocarcinoma cells with and without N130 expression by culturing in two different medium as described above, were un-treated or treated with 20nM paclitaxel for 72-hour. The cells that remained alive after paclitaxel treatment were harvested at the end of paclitaxel treatment.

2.3 Peptide extraction

The cell pallets were collected and sonicated. Protein concentration was measured by BCA assay. The same amounts of proteins (1 mg) from each condition were denatured in 8M urea in 0.4M NH4HCO3, 0.1% (w/v) SDS solution (pH8.3), and 10mM TCEP (Tris (2-carboxythyl) phosphine) by incubation at 60°C for 1 hour. Proteins were alkylated with 16mM iodoacetamide by incubation at room temperature in the dark for 30 min. The sample was diluted 4-fold by trypsin digestion buffer (100mM NH4HCO3, pH8.3). Trypsin was added at a 1 to 50 part sample protein excess and allowed to digest at 37°C overnight. SDS-PAGE and silver staining was employed to ensure the completion of tryptic digest. The peptides were purified with C18 Sep-Pak Vac columns and resuspended in water with a final concentration of 10μg/μl.

2.4 iTRAQ labeling

Tryptic peptides (50 μg) from each sample were mixed with 20 μl of dissolution buffer provided with iTRAQ kit. The iTRAQ 4-plex reagents were dissolved in 70μl of methanol respectively and strongly vortexed. Each iTRAQ labeling reagent was then added to the sample and mixed. The mixture was incubated at room temperature for 1 hour followed by cleaning up by SCX column.

2.5 Mass spectrometry analysis

For protein quantification by spectral count, each peptide mixture was analyzed twice by the LTQ ion trap mass spectrometer (Thermo Finnigan, San Jose, CA). For protein identification and quantitative analysis using LC-MS, an ESI-QSTAR mass spectrometer (Applied Biosystems, Foster City, CA) was used. In both systems, 2 μl (2 μg) peptides were injected into a peptide cartridge packed with C18 resin, and then passed through a 10 cm × 75 μm i.d. microcapillary HPLC (μLC) column packed with C18 resin. The effluent from the μLC column entered an electrospray ionization source in which peptides were ionized and passed directly into the mass spectrometers. A linear gradient of acetonitrile from 5%–32% over 100 min at flow rate of ~300 nL/min was applied. During the LC-MS mode, data was acquired in the m/z range of 400 and 2000. The MS/MS was also turned on to collect CID using data dependent mode. Each sample was analyzed three times by QSTAR to increase the accuracy of quantification.

iTRAQ labeled peptide was analyzed by both QSTAR and 2-D LC (Nano, Eksigent, Dublin, CA) MALDI TOF/TOF (ABI 4800, Applied Biosystems, Foster City, CA). The analysis on the QSTAR was performed in the same setting as described above. For the analysis by 2-D Nano LC and MALDI 4800-TOF/TOF, on-line integration of 15-cm-long 300μm strong cation exchange column (SCX) with 15-cm-long 300 μm of C18-reverse phase liquid chromatography (RPLC) was employed. Four SCX fractions of 0, 5, 50 and 500mM KCl and 3–45% linear acetonitrile gradient (containing 0.1% TFA and acetonitrile) of RPLC for each fraction were applied before analysis by MALDI-TOF/TOF. Peptides eluted from columns were directly mixed with CHCA and spotted on a MALDI target plate with 768 spots followed by analysis with MS and MS/MS using the ABI 4800 MALDI-TOF/TOF.

2.6 Peptide identifications

The iTRAQ data analyzed either by QSTAR or MALDI and label-free data from LTQ were searched by ProteinPilot software 2.0 [19] against the human International Protein Index database (IPI, version 2.28) using the cut-off probability score of 0.9.

Tandem MS spectra of label-free peptides from the QSTAR were searched with SEQUEST [20] against the same human IPI protein database (version 2.28). The peptide mass tolerance is 2.0Da. Other parameters of database searching are modified as following: cysteine modification (add cysteine 57) and oxidized methionine (add methionine with 16 Da). The output files were evaluated by INTERACT [21] and ProteinProphet [22]. The cutoff of ProteinProphet analysis is the probability score ≥ 0.9 so that low probability protein identifications can be filtered out. For each identified peptide, peptide sequence, protein name, precursor m/z value, peptide mass, charge state, retention time where the MS/MS was acquired, and probability of the peptide identification being correct were recorded and outputted using INTERACT [21].

2.7 Quantitative proteomic analyses

The ratio of the four channels of iTRAQ labeling was determined by the ProteinPilot software.

A suite of software tools of SpecArray were used to analyze the LC-MS data as described previously [23]. For each peptide peak, an abundance ratio of matched peptides in different samples was determined for each peptide peak. An in-house Perl script was then used to link the peptide identification from MS/MS spectra to their corresponding MS peaks by matching precursor mass within 1 Da, retention time within 10 min, and charge state of the peptides.

The identified peptides from LTQ with a probability score ≥ 0.9 were used for the spectral count. To determine the number of MS/MS spectra used for identification of each protein in different conditions using our in-house developed software tool. For peptide sequence that could come from multiple proteins, the spectral count is equally distributed to all proteins with the identified peptide. Due to random sampling of mass spectrometer in collecting MS/MS spectra used for spectral count, we only quantified proteins with at least 4 spectral counts in total from the four cell states.

2.8 Evaluation of the cut-off of protein abundance ratio for proteins changes

To correct for any systematic errors of protein ratio introduced by sample handling and to determine the appropriate cut-off for protein changes, the distribution of abundance ratios in different cell states was generated for each quantitative method. Since the majority of proteins were not expressed differently in two cell states, we normalized the ratio based on the distribution of the protein abundance ratios from two cell states. Proteins fell out of the normal distribution from the abundance ratio of two cell states were considered as altered proteins. The threshold to select protein changes was based on the ratio distribution of two cell states. The mean and standard deviation of ratio from two cell states were calculated, and the abundance of proteins with an abundance ratio outside of one standard deviation from the mean were flagged as altered.

2.9 Cellular component classification of changed genes

To classify the changed proteins into cellular component, GO (Gene Ontology) [24] analysis (http://www.godatabase.org/dev) was performed. All the identified and quantified proteins by iTRAQ quantitation were used as background. Protein changes due to paclitaxel quantified by iTRAQ were used as changed proteins. P value was calculated using one-side Fisher exact test. To correct for multiple testing errors, p value was adjusted the minimum P method of Westfall and Young[25].

3 Results and discussion

3.1 Inducible expression of N130 in SKOV-3 cells

To identify proteins related to paclitaxel treatment and resistance, SKOV-3 cells with inducible expression of N130 protein [6, 26] was used in this study. The quantitative proteomic analyses of paclitaxel treatment for SKOV-3 N130 cells with and without expression of N130 are schematically illustrated in Figure 1 and consist of four steps: 1) two dishes were treated to induce the expression of N130 and two dishes were untreated as controls; 2) One dish with expression of N130 and one dish without expression of N130 were treated with paclitaxel, and the other two dishes were not treated with paclitaxel act as controls; 3) the peptides were extracted from cell lysate of the four cell states by sonication and trypsin digestion; 4) the tryptic peptides were identified and quantified by iTRAQ labeling, LC-MS, and spectral count.

Figure 1.

Figure 1

Flowchart of the quantitative study of Nac1 function and paclitaxel treatment

To evaluate the expression of N130, the florescence of EGFP was monitored as an indicator to determine whether N130 was induced after removing doxycycline from culture medium. SKOV-3 N130 cells were observed after 28-hour culture in medium with and without doxycycline according to our previous study [6, 26]. The cells were observed under florescence microscope (Figure 2A and B). The induced expression of N130/EGFP by doxycycline withdrawal was indicated by the green fluorescent (Figure 2D), which was not observed in cells cultured with doxycycline (shown in Figure 2C). Thus the expression of N130 can be robustly induced in SKOV-3 N130 cells.

Figure 2.

Figure 2

Expression of fusion protein N130-EGFP in SKOV-3 ovarian cell line: A) SKOV-3 N130 cultured with doxycycline, the N130-EGFP expression was off; B) SKOV-3 N130 cultured without doxycycline, the N130-EGFP expression was on; C) no fluorescent came from SKOV-3 N130 cultured with doxycycline; D) fluorescent came from SKOV-3 N130 cultured without doxycycline.

3.2 Quantitative proteomic analyses to identify protein changes

To determine the protein changes related to paclitaxel treatment and Nac1 function, the ovarian cancer cells with and without Nac1 function were treated with paclitaxel. After treated with 20nM paclitaxel for 72-hour, around 60 % cells was alive, which were considered as cells resistant to paclitaxel. The cells that remained alive after paclitaxel treatment were harvested at the end of paclitaxel treatment. The cell pallets were sonicated and 1 mg proteins from each cell states were digested by trypsin, followed by quantitative proteomic analysis using iTRAQ. A portion of tryptic peptides (50 μg) from N130-ON without paclitaxel treatment (ON−T), N130-ON with paclitaxel treatment (ON+T), N130-OFF without paclitaxel treatment (OFF−T) and N130-OFF with paclitaxel treatment (OFF+T) cells were labeled with 114, 115, 116 and 117 of iTRAQ reagents and analyzed by LC-MALDI TOF/TOF and LC-QSTAR for quantitative proteomic analysis. We were able to identify and quantify 850 proteins using iTRAQ labeling.

We then determined the protein changes in two cell states quantified by iTRAQ. When cells are induced of N130 expression or treated of paclitaxel, majority of cellular proteins are expected to be not affected and stay in the same level [27]. However, due to errors introduced by analytical procedures, such as sample handling and quantification process, the protein ratio from majority of proteins may be shifted. To determine the proteins with abundance changes in two cell states, histogram was used to generate the number of proteins in different abundance ratio (Figure 3). The threshold was set as < 0.7 and >1.3 for iTRAQ labeling. Majority of proteins (554 proteins, 65%) were distributed within one standard deviation (0.25) from the mean (1.046) and were considered as unchanged. Proteins that fell out of one standard deviation of the normal distribution curve were considered as with changed. A total of 296 proteins were changed due to paclitaxel treatment (160 proteins) or Nac1 inactivation (93 proteins) or both of paclitaxel treatment and Nac1 inactivation (181 proteins).

Figure 3.

Figure 3

The histogram analysis of peptide ratios of different cell states quantified by iTRAQ

Nac1 was determined as changed upon the induced expression of N130. A total of seven peptides were identified and quantified from Nac1, and all the identified peptides were located in the N-terminus 1-130 amino acids (with 71% sequence coverage of N 130), indicating that the identified peptides were likely from overexpressed N130 instead of endogenous Nac1 protein. The amount of N130 in N130 ON cells was measured as about 10-fold higher than it in N130 OFF cells (Table 1). The quantitative results of N130 expression confirmed that: 1) the inducible Tet-OFF system was efficient in inducing N130 expression; 2) the iTRAQ quantitative methods was able to determine the relative abundance of proteins and could be used for identification of other protein changes.

Table 1.

Overexpression of N130 determined by three quantitative proteomic methods. For iTRAQ quantitation, all ratios are normalized to the reporter ion at m/z 116 (OFF-T). For LC-MS quantitation, the number showed the peak intensities.

Methods OFF−T OFF+T ON−T ON+T
iTRAQ 1.00 1.08±0.41 10.73±2.87 12.13±5.21
LC-MS 0.00±0.00 0.00±0.00 2628.84±167.78 3244.69±335.38
Spectral count 0.00 5.00 80.13 83.00

3.3 Altered proteins related to Nac1 function and paclitaxel treatment

The 296 unique protein alterations determined by iTRAQ labeling were listed in Table 2 and grouped into 3 classes: 1) Protein changes upon paclitaxel treatment (OFF−T vs. OFF+T). These include proteins elevated upon paclitaxel treatment such as tubulin beta-5 chain, tubulin alpha-4 chain, mitochondrial proteins such as cytochrome c and ATP synthase, mitochondrial inner membrane protein, acute-phase proteins such as hemoglobin, cell surface antigens such as CD44 and 4F2 cell surface antigen, etc., and proteins with decreased abundance upon paclitaxel treatment such as seven subunits of ribosomal proteins, proteins regulating cell meiosis, mitosis and postmitotic functions such as mitogen-activated protein kinase3, etc. 2) Changed protein expression upon inactivation of Nac1 (OFF−T vs. ON−T). Induced expression of N130 (ON−T) inhibits the function of Nac1. Since Nac1 is a potential transcriptional repressor [26], the proteins with altered expression after Nac1 inhibition could be controlled by Nac1. These proteins include Ras-related protein Rab-8, transcription repressor, eukaryotic translation initiation factor 3, etc. 3) Changes of protein abundance upon paclitaxel treatment and induced N130 expression (OFF−T vs. ON+T), and proteins in this class might associate with the function of Nac1 gene in the response to paclitaxel treatment. Proteins in this class include Ras GTPase-activating-like protein IQGAP1, polyadenylate-binding protein 1, etc.

Table 2.

Proteins associated with paclitaxel treatment with and without N130 overexpression determined by iTRAQ

IPI ProteinName Swiss-Prot % Cov OFF+T/OFF−T ON−T/OFF−T ON+T/OFF−T
Proteins regulated upon paclitaxel treatment
IPI00298971 Vitronectin (S-protein) (V75) P04004 3.1 2.54 1.44 2.50
IPI00328696 Hemoglobin alpha chain P01922 21.2 2.45 1.20 2.78
IPI00305185 Stromal cell protein Q9BRV3 10.9 2.37 1.67 3.15
IPI00028481 Ras-related protein Rab-8 P24407 12.1 2.32 3.32 2.16
IPI00160897 Hypothetical protein Q969E5 37.6 2.05 1.08 2.27
IPI00396589 Interleukin enhancer binding factor 2, 45kD Q9BWD4 11.5 2.01 0.88 1.63
IPI00026087 Barrier-to-autointegration factor O75531 15.7 1.84 2.47
IPI00299149 Ubiquitin-like protein SMT3B P55855 32.6 1.83 1.48 1.08
IPI00218816 beta globin P02023 19.7 1.81 2.10 2.49
IPI00219219 beta-galactosidase binding lectin P09382 65.2 1.81 1.14 1.39
IPI00030131 Splice isoform Beta of P42167 Thymopoietin, isoforms beta/gamma P42167 14.1 1.81 1.23 2.11
IPI00009346 Protein C6orf53 Q9P0S9 13.4 1.80
IPI00002520 Serine hydroymethyltransferase, mitochondrial P34897 12.5 1.79 1.13 1.34
IPI00013452 Bifunctional aminoacyl-tRNA synthetase P07814 4.2 1.78 1.30 1.28
IPI00027192 Procollagen-lysine,2-ooglutarate 5-dioygenase 1 Q02809 3.9 1.77 2.10 0.84
IPI00329705 KIAA1363 protein Q86WZ1 9.8 1.76 1.18 2.77
IPI00374657 vesicle-associated membrane protein-associated protein A isoform 1 4.9 1.74 1.49 0.97
IPI00236879 Atain-2 related domain protein Q8WWM7 7.1 1.74 1.51 1.84
IPI00219291 ATP synthase f chain, mitochondrial P56134 37.9 1.73 1.15 1.73
IPI00015786 Spectrin alpha chain, brain Q13813 11.2 1.71 1.41 1.89
IPI00382733 Transcription repressor O75799 6.6 1.68 1.84 1.19
IPI00025273 Trifunctional purine biosynthetic protein adenosine-3 P22102 10.1 1.68 1.43 1.57
IPI00006558 CGI-61 protein Q9NR47 10.1 1.67 1.32 0.75
IPI00011229 Cathepsin D P07339 14.1 1.66 1.02 1.61
IPI00305064 CD44 P16070 6.3 1.65 1.26 1.65
IPI00025874 Dolichyl-diphosphooligosaccharide--protein glycosyltransferase 67 kDa subunit P04843 15.7 1.65 1.20 1.48
IPI00029557 GrpE protein homolog 1, mitochondrial Q9HAV7 18.4 1.65 1.30 1.46
IPI00290889 DNA topoisomerase I P11387 2.4 1.64 1.01 1.27
IPI00009960 Mitochondrial inner membrane protein Q16891 7.4 1.64 1.20 1.05
IPI00218682 P13674 Prolyl 4-hydroylase alpha-1 subunit P13674 5.4 1.62 0.82 1.21
IPI00015148 Ras-related protein Rap-1b P09526 14.7 1.61 1.11 1.44
IPI00215916 cytochrome c P00001 29.5 1.61 1.21 1.73
IPI00016572 Small nuclear ribonucleoprotein G Q15357 17.1 1.60 1.52 1.77
IPI00218019 Basigin long isoform Q8IZL7 9.6 1.59 0.84 1.57
IPI00005202 Membrane associated progesterone receptor component 2 O15173 10.3 1.59 1.14 2.17
IPI00009236 Caveolin-1 Q03135 24.2 1.56 1.22 1.66
IPI00019472 Neutral amino acid transporter B(0) Q15758 7.4 1.55 0.68 1.19
IPI00031522 Trifunctional enzyme alpha subunit, mitochondrial P40939 4.7 1.55 0.80 1.19
IPI00216492 P31942 Heterogeneous nuclear ribonucleoprotein H3 P31942 10.9 1.55 1.14 1.35
IPI00219835 P04895 Guanine nucleotide-binding protein G(S), alpha subunit P04895 23.9 1.55 1.06 1.64
IPI00016447 Hypothetical protein FLJ20502 Q9N08 13.1 1.53 1.08 2.08
IPI00220739 progesterone receptor membrane component 1 O00264 27.7 1.52 1.08 1.42
IPI00025019 Proteasome subunit beta type 1 P20618 21.2 1.52 1.00 0.64
IPI00217952 Glucosamine--fructose-6-phosphate aminotransferase [isomerizing] 1 Q06210 8.6 1.51 0.96 1.52
IPI00014238 Lysyl-tRNA synthetase Q15046 6.7 1.50 1.49
IPI00385566 Hypothetical protein FLJ30014 Q969M3 3.5 1.50 1.62 1.56
IPI00300074 Phenylalanyl-tRNA synthetase beta chain Q9NSD9 10.7 1.48 1.17 0.97
IPI00141318 P63 protein Q07065 17.6 1.47 1.07 1.25
IPI00142634 Tubulin beta-5 chain P05218 43 1.47 1.00 1.47
IPI00017726 3-hydroyacyl-CoA dehydrogenase type II Q99714 15.3 1.47 1.04 1.74
IPI00014230 Complement component 1, Q subcomponent binding protein, mitochondrial Q07021 19.9 1.47 1.17 1.25
IPI00005159 Actin-like protein 2 O15142 19.3 1.47 1.22 1.00
IPI00107117 Peptidylprolyl isomerase B P23284 25.9 1.47 1.32 1.72
IPI00024911 Endoplasmic reticulum protein ERp29 P30040 5 1.46 1.10 1.27
IPI00220985 Keratin, type I cytoskeletal 18 P05783 39.6 1.46 0.94 1.49
IPI00180128 Similar to KIAA0005 gene product Q9BUY0 10.4 1.45 1.11 1.29
IPI00383671 serologically defined breast cancer antigen 84 isoform a Q9Y282 7.2 1.45 1.21 1.10
IPI00395917 ferritin heavy chain P02794 14.8 1.44 0.87 1.87
IPI00374410 cytochrome b5 reductase soluble isoform P003781-2 26.6 1.43 0.90 1.26
IPI00031479 Protein disulfide isomerase A5 Q14554 2.3 1.43 0.88 1.54
IPI00028055 Transmembrane protein Tmp21 P49755 22.4 1.41 1.11 1.32
IPI00303882 Cargo selection protein TIP47 O60664 27.2 1.41 1.26 1.60
IPI00384489 Similar to adaptor-related protein comple 1, beta 1 subunit Q10567 8.7 1.41 1.14 1.24
IPI00021766 Splice isoform 1 of Q9NQC3 Reticulon 4 Q9NQC3 7.1 1.41 1.09 1.32
IPI00011937 Peroiredoin 4 Q13162 17.7 1.41 1.73
IPI00003965 Ubiquitin carboyl-terminal hydrolase 7 Q93009 1.9 1.40 1.08 1.65
IPI00024919 Thioredoin-dependent peroide reductase, mitochondrial P30048 25.4 1.40 1.05 1.39
IPI00386755 ERO1 (S. cerevisiae)-like Q96HE7 3 1.39 1.06 1.21
IPI00219604 mitogen-activated protein kinase kinase 1 Q02750 7.6 1.39 0.99 1.36
IPI00009407 Defender against cell death 1 P46966 14.2 1.39 1.13 1.94
IPI00004902 Electron transfer flavoprotein beta-subunit P38117 14.1 1.38 1.03 1.06
IPI00024993 Enoyl-CoA hydratase, mitochondrial P30084 14.1 1.38 0.81 1.12
IPI00046828 similar to CG15881-PB Q4VC31 14.5 1.37 1.33
IPI00021954 Golgi-specific brefeldin A-resistance guanine nucleotide echange factor 1 Q92538 7 1.37 0.87 1.43
IPI00165092 Hypothetical protein FLJ13995 Q9H817 6.3 1.37 0.87 1.61
IPI00027493 4F2 cell-surface antigen heavy chain P08195 22.9 1.37 0.97 1.36
IPI00007750 Tubulin alpha-4 chain P05215 40 1.37 1.04 1.29
IPI00009329 Utrophin P46939 1.3 1.37 1.44 0.92
IPI00216393 Splice isoform Non-brain of P09496 Clathrin light chain A P09496 9.6 1.37 1.37 1.09
IPI00337814 Hypothetical protein Q9BWL4 11.7 1.37 1.16 1.65
IPI00026167 NHP2-like protein 1 P55769 24.2 1.37 0.86 1.46
IPI00396321 Hypothetical protein Q9P189 25.7 1.36 1.00 1.17
IPI00386621 Similar to calmodulin 2 Q9BRL5 16.3 1.36 1.44 1.10
IPI00029574 Putative S100 calcium-binding protein A11 pseudogene O60417 10.8 1.36 0.85 1.26
IPI00027423 Serine/threonine protein phosphatase PP1-alpha 1 catalytic subunit P08129 10.6 1.36 1.07 1.05
IPI00007682 Vacuolar ATP synthase catalytic subunit A, ubiquitous isoform P38606 6.3 1.35 0.90 1.07
IPI00160021 HIRA-interacting protein 5 Q9UMS0 10.2 1.35 1.17 1.10
IPI00291005 cytosolic malate dehydrogenase P40925 9.9 1.34 0.46 0.95
IPI00007928 PRP8 protein O14547 6.2 1.34 1.02 0.96
IPI00220834 ATP-dependant DNA helicase II P13010 14.1 1.34 1.38 0.91
IPI00016513 Ras-related protein Rab-10 O88386 8 1.34 1.07 1.18
IPI00009950 Vesicular integral-membrane protein VIP36 Q12907 6.7 1.34 0.92 1.17
IPI00029079 GMP synthase [glutamine-hydrolyzing] P49915 9.6 1.33 2.70 2.50
IPI00297084 Dolichyl-diphosphooligosaccharide--protein glycosyltransferase 48 kDa subunit P39656 12.7 1.33 1.32 1.41
IPI00290142 CTP synthase P17812 12.5 1.33 1.10 1.42
IPI00009922 DC50 Q9GZT3 12.8 1.33 1.01 1.42
IPI00006865 Vesicle trafficking protein SEC22B O75396 11.2 1.33 1.61
IPI00293102 Splice isoform 2 of Q15257 Protein phosphatase 2A, regulatory subunit B′ Q15257 12.8 1.33 1.05 1.45
IPI00008240 Methionyl-tRNA synthetase P56192 7.8 1.33 1.09 1.20
IPI00220855 Similar to H2A histone family, member O Q9BTM1 58.9 1.33 0.89 1.42
IPI00186338 unnamed protein 41.9 1.33 1.09 1.30
IPI00329351 60 kDa heat shock protein, mitochondrial P10809 31 1.33 1.05 1.31
IPI00007765 Stress-70 protein, mitochondrial P38646 28.4 1.33 1.05 1.12
IPI00026328 Thioredoin-like protein p19 O95881 5.2 1.33 1.30 1.32
IPI00027434 Transforming protein RhoC P08134 19.7 1.32 1.22 1.09
IPI00005270 Hypothetical protein Q9BVD2 1.8 1.32 2.46 1.41
IPI00003815 Rho GDP-dissociation inhibitor 1 P52565 16.7 1.32 0.97 1.22
IPI00024364 Importin beta-2 subunit Q92973 7.3 1.31 0.61 1.21
IPI00006482 Sodium/potassium-transporting ATPase alpha-1 chain P05023 10.8 1.31 1.31 1.33
IPI00021785 Cytochrome c oidase polypeptide Vb, mitochondrial P10606 25.6 1.31 1.38 2.03
IPI00304802 Dihydrolipoamide succinyltransferase component of 2-ooglutarate dehydrogenase comple, mitochondrial P36957 11.9 1.31 0.92 1.23
IPI00027252 repressor of estrogen receptor activity Q9BV3 15.1 1.31 0.96 1.54
IPI00328188 fatty acid synthase Q96IT0 9.8 1.31 0.96 1.11
IPI00027717 Component of gems 4 P57678 4.6 1.30 1.17 1.76
IPI00022793 Trifunctional enzyme beta subunit, mitochondrial P55084 13.5 1.30 1.29 1.35
IPI00021978 Peroisome assembly factor O96011 6.2 1.30 0.77 0.78
IPI00008167 Sodium/potassium-transporting ATPase beta-3 chain P54709 10.4 1.30 0.96 1.26
IPI00334713 Heterogeneous nuclear ribonucleoprotein A/B Q99729 21.4 0.69 0.00 1.21
IPI00219158 ribosomal protein L29 P47914 30.8 0.69 1.17 0.53
IPI00033904 similar to ribosomal protein S3a P61247 26.5 0.69 1.03 0.63
IPI00004860 Arginyl-tRNA synthetase P54136 6.8 0.69 1.02 0.88
IPI00219446 prostatic binding protein P30086 20.3 0.68 0.43 0.54
IPI00374260 ribosomal protein L10 P27635 30.4 0.68 0.98 0.69
IPI00052229 Hypothetical protein Q9UG74 6.7 0.68 0.98 2.48
IPI00005680 Hypothetical protein KIAA0095 Q14705 6.2 0.68 1.48 0.73
IPI00385244 phosphoglycerate mutase 1 (brain) P18669 23.2 0.68 1.15 1.20
IPI00002149 GTP-binding protein SAR1b Q9Y6B6 7.1 0.67 1.21 0.81
IPI00165164 Similar to ubiquitin-conjugating enzyme E2I Q9BQ25 16.8 0.67 0.90 1.32
IPI00215719 60S ribosomal protein L18 Q07020 31 0.67 1.10 0.55
IPI00027463 Calcyclin P06703 24.4 0.66 1.11 0.58
IPI00021828 Cystatin B P04080 44.9 0.66 0.84 0.72
IPI00182728 SKD1 protein O75351 8.2 0.66 0.93 1.24
IPI00008524 Polyadenylate-binding protein 1 P11940 23.3 0.66 0.97 0.56
IPI00216237 ribosomal protein L36 Q9Y3U8 14.3 0.66 1.04 0.80
IPI00219520 UNR protein O75534 5.6 0.66 1.03 0.63
IPI00395748 Cytosolic acyl coenzyme A thioester hydrolase O00154 14.8 0.65 1.04 0.84
IPI00186712 40S ribosomal protein S26 P02383 20.6 0.65 1.20 0.65
IPI00170935 Hypothetical protein KIAA1185 Q8N1G4 28.3 0.65 1.14 0.98
IPI00032826 Hsc70-interacting protein P50502 13 0.65 1.02 0.60
IPI00220067 leucine aminopeptidase P28838 8.1 0.65 0.80 0.87
IPI00215790 60S ribosomal protein L38 P23411 19.3 0.65 0.93 0.62
IPI00395865 Histone acetyltransferase type B subunit 2 Q16576 5 0.64 0.69 0.84
IPI00000861 LIM and SH3 domain protein 1 Q14847 9.2 0.64 0.71 0.48
IPI00165486 similar to ribosomal protein S2 9.6 0.63 0.93 0.28
IPI00184821 Bifunctional coenzyme A synthase (CoA synthase) Q13057 11.7 0.63 0.45 1.26
IPI00217709 DNA topoisomerase II, beta isozyme Q02880 6.2 0.63 1.08 1.00
IPI00018768 Translin Q15631 5.3 0.63 0.99 0.71
IPI00011603 26S proteasome non-ATPase regulatory subunit 3 O43242 8.6 0.60 1.01 0.37
IPI00396417 MHC class I antigen Q861B7 6.3 0.57 1.20 1.31
IPI00334922 Hypothetical protein FLJ10519 Q9NVT5 8.8 0.57 0.69 0.90
IPI00382700 Filamin B O75369 7.1 0.55 0.90 0.66
IPI00296635 1,4-alpha-glucan branching enzyme Q04446 2.5 0.52 0.97 0.69
IPI00219486 40S ribosomal protein S24 P16632 9.2 0.52 1.05 0.56
IPI00163230 COP9 signalosome subunit 6 O15387 12.8 0.52 0.59 0.60
IPI00303063 KIAA0648 protein Q96DB6 8.9 0.51 0.92 0.78
IPI00382617 P37 AUF1 Q12771 11.5 0.50 0.77 0.71
IPI00384261 Muscleblind-like protein EP40s Q86UV9 7.3 0.43 1.24 0.89
IPI00295386 carbonyl reductase 1 P16152 10.5 0.33 0.77 1.17
IPI00385399 mitogen-activated protein kinase 3 P27361 18.7 0.05 0.06 0.11
IPI00218547 Delta 1-pyrroline-5-carboylate synthetase P54886 13 0.00 0.82 0.00
Proteins regulated upon inactivation of Nac1
IPI00028481 Ras-related protein Rab-8 P24407 12.1 2.32 3.32 2.16
IPI00029079 GMP synthase [glutamine-hydrolyzing] P49915 4.5 1.33 2.70 2.50
IPI00026087 Barrier-to-autointegration factor O75531 15.7 1.84 2.47
IPI00005270 Hypothetical protein Q9BVD2 1.8 1.32 2.46 1.41
IPI00027192 Procollagen-lysine,2-oxoglutarate 5-dioxygenase 1 Q02809 3.9 1.77 2.10 0.84
IPI00218816 beta globin P02023 19.7 1.81 2.10 2.49
IPI00259901 similar to peptidylprolyl isomerase A (cyclophilin A) Q68J44 8.5 0.88 2.07 0.59
IPI00290416 Splice isoform 1 of Q9NTK5 Putative GTP-binding protein PTD004 Q9NTK5 9.1 1.17 1.92 1.02
IPI00382733 Transcription repressor O75799 6.6 1.68 1.84 1.19
IPI00015953 Nucleolar RNA helicase II Q9NR30 18.7 0.95 1.82 1.43
IPI00011937 Peroxiredoxin 4 Q13162 17.7 1.41 1.73
IPI00215736 Alpha enolase P06733 45 1.00 1.67 0.83
IPI00305185 Stromal cell protein Q9BRV3 10.9 2.37 1.67 3.15
IPI00005648 Scaffold attachment factor B2 Q14151 5 1.13 1.67 1.25
IPI00003565 26S proteasome non-ATPase regulatory subunit 10 O75832 4 0.90 1.63 1.05
IPI00385566 Hypothetical protein FLJ30014 Q969M3 3.5 1.50 1.62 1.56
IPI00006865 Vesicle trafficking protein SEC22B O75396 11.2 1.33 1.61
IPI00016339 Ras-related protein Rab-5C P51148 27.3 0.96 1.60 0.86
IPI00021383 Heterogeneous nuclear ribonucleoprotein A3 P51991 19 1.03 1.58 0.67
IPI00029485 Splice isoform p150 of Q14203 Dynactin 1 Q14203 5.8 1.07 1.56
IPI00302925 T-complex protein 1, theta subunit P50990 24.1 1.02 1.54 0.86
IPI00008918 Splice isoform Beta of Q9UHB6 Epithelial protein lost in neoplasm Q9UHB6 7.2 0.98 1.52 0.64
IPI00016572 Small nuclear ribonucleoprotein G Q15357 17.1 1.60 1.52 1.77
IPI00236879 Ataxin-2 related domain protein Q8WWM7 7.1 1.74 1.51 1.84
IPI00301434 similar to My016 protein Q9H3K6 13.8 1.03 1.51
IPI00014238 Lysyl-tRNA synthetase Q15046 6.7 1.50 1.49
IPI00374657 vesicle-associated membrane protein-associated protein A isoform 1 4.9 1.74 1.49 0.97
IPI00176799 similar to hypothetical protein Q8N3B3 12.2 1.04 1.48 0.88
IPI00299149 Ubiquitin-like protein SMT3B P55855 32.6 1.83 1.48 1.08
IPI00005680 Hypothetical protein KIAA0095 Q14705 6.2 0.68 1.48 0.73
IPI00215802 Splice isoform Short of P23152 Splicing factor, arginine/serine- rich 3 P23152 29 0.79 1.44 0.81
IPI00009329 Utrophin P46939 1.3 1.37 1.44 0.92
IPI00298971 Vitronectin (Serum spreading factor) (S-protein) (V75) P04004 8.2 2.54 1.44 2.50
IPI00295589 Eukaryotic translation initiation factor 4GI Q96I65 9.2 1.12 1.44 1.63
IPI00386621 Similar to calmodulin 2 Q9BRL5 16.3 1.36 1.44 1.10
IPI00025273 Splice isoform Long of P22102 Trifunctional purine biosynthetic pro P22102 10.1 1.68 1.43 1.57
IPI00015947 DnaJ homolog subfamily B member 1 P25685 11.2 0.94 1.42 0.80
IPI00148062 Nuclear-associated protein SPAN-Xb Q9NS25 43.7 1.25 1.42 1.36
IPI00382644 Putative eukaryotic translation initiation factor 1A O75642 13.3 0.99 1.41 0.92
IPI00015786 Spectrin alpha chain, brain Q13813 11.2 1.71 1.41 1.89
IPI00107357 Cleft lip and palate associated transmembrane protein 1 Q9BSS5 8.2 0.99 1.40 1.08
IPI00307162 VCL isoform meta-VCL P18206 12.1 0.92 1.40 0.79
IPI00219156 ribosomal protein L30 P04645 10.4 0.83 1.39 1.13
IPI00168388 Splice isoform 1 of Q9UHB9 Signal recognition particle 68 kDa protein Q9UHB9 9.3 1.15 1.39 1.23
IPI00021785 Cytochrome c oxidase polypeptide Vb, mitochondrial P10606 25.6 1.31 1.38 2.03
IPI00220834 ATP-dependant DNA helicase II P13010 13.3 1.34 1.38 0.91
IPI00017596 Microtubule-associated protein RP/EB family member 1 Q15691 10.4 0.97 1.37 0.96
IPI00006328 ATPase inhibitor, mitochondrial Q9UII2 17.9 1.13 1.37 0.94
IPI00186711 Similar to plectin 1, intermediate filament binding protein, 500kD Q96IE3 22.2 0.98 1.37 1.03
IPI00216393 Splice isoform Non-brain of P09496 Clathrin light chain A P09496 9.6 1.37 1.37 1.09
IPI00293350 Translin-associated protein X Q99598 5.5 1.05 1.36 1.17
IPI00008552 Thioredoxin-like protein 2 O76003 12.8 1.02 1.35 0.71
IPI00332570 Polyadenylate-binding protein 2 Q15097 9.2 1.01 1.34 1.27
IPI00218606 40S ribosomal protein S23 P39028 23.2 0.90 1.34 0.77
IPI00377199 Histone H2B.d Q99877 48.7 1.01 1.34 0.99
IPI00304925 Heat shock 70 kDa protein 1 P08107 36.3 0.97 1.34 0.81
IPI00182373 Splice isoform IIa of O15460 Prolyl 4-hydroxylase alpha-2 subunit O15460 9.8 0.97 1.34 0.70
IPI00084495 similar to ribosomal protein S15 18.3 1.09 1.33 1.19
IPI00217468 H1 histone family, member 5 P16401 19.5 0.98 1.33 0.68
IPI00383500 Splice isoform 2 of Q96AC1 Pleckstrin homology domain contain Q96AC1 1.7 1.08 1.33 1.29
IPI00107117 Peptidylprolyl isomerase B P23284 25.9 1.47 1.32 1.72
IPI00006558 CGI-61 protein Q9NR47 10.1 1.67 1.32 0.75
IPI00297084 Dolichyl-diphosphooligosaccharide--protein glycosyltransferase 48 k P39656 12.7 1.33 1.32 1.41
IPI00240812 Hypothetical protein KIAA0979 Q9Y2I5 7.4 0.85 1.32 0.71
IPI00376295 mitogen-activated protein kinase 1 P28482 13.9 0.95 1.32 1.22
IPI00396171 microtubule-associated protein 4 isoform 2 P27816-1 11.6 0.81 1.31 1.14
IPI00006482 Splice isoform Long of P05023 Sodium P05023 10.8 1.31 1.31 1.33
IPI00026328 Thioredoxin-like protein p19 O95881 5.2 1.33 1.30 1.32
IPI00029557 GrpE protein homolog 1, mitochondrial Q9HAV7 18.4 1.65 1.30 1.46
IPI00032313 Placental calcium-binding protein P26447 29.7 1.08 0.69 0.87
IPI00395865 Histone acetyltransferase type B subunit 2 Q16576 5 0.64 0.69 0.84
IPI00005728 RER1 protein O15258 7.7 1.09 0.69 0.94
IPI00334922 Hypothetical protein FLJ10519 Q9NVT5 8.8 0.57 0.69 0.90
IPI00019472 Neutral amino acid transporter B(0) Q15758 6.7 1.55 0.68 1.19
IPI00017292 Splice isoform 1 of P35222 Beta-catenin P35222 9.1 1.09 0.68 0.68
IPI00014197 Hypothetical protein Q9UKY7 11.2 0.93 0.66 0.73
IPI00010810 Electron transfer flavoprotein alpha-subunit, mitochondrial P13804 24.6 1.21 0.63 1.62
IPI00013068 Eukaryotic translation initiation factor 3 subunit 6 Q64252 4.5 0.85 0.63 1.01
IPI00396373 BLOCK 23 Q8NHW5 4.1 1.20 0.62 0.92
IPI00024364 Importin beta-2 subunit Q92973 7.3 1.31 0.61 1.21
IPI00216298 thioredoxin P10599 41 0.71 0.61 1.09
IPI00032406 DnaJ homolog subfamily A O60884 5.3 0.76 0.60 0.78
IPI00163230 member 2 COP9 signalosome subunit 6 O15387 12.8 0.52 0.59 0.60
IPI00030940 Protein KIAA0052 P42285 2.6 0.74 0.59 1.18
IPI00154645 Similar to hypothetical protein FLJ12085 Q9HA83 2.3 0.94 0.57 0.92
IPI00395750 Splice isoform Long of O75083 WD-repeat protein 1 O75083 9.2 0.86 0.52 0.93
IPI00328193 Hypothetical protein Q8WVM8 5.9 1.00 0.50 1.00
IPI00291005 cytosolic malate dehydrogenase P40925 3.9 1.34 0.46 0.95
IPI00184821 Bifunctional coenzyme A synthase (CoA synthase) (NBP) (POV-2) Q13057 11.7 0.63 0.45 1.26
IPI00219624 proteasome alpha 3 subunit isoform 1 P25788 4.7 1.29 0.45 1.22
IPI00219446 prostatic binding protein P30086 20.3 0.68 0.43 0.54
IPI00385399 mitogen-activated protein kinase 3 18.7 0.05 0.06 0.11
IPI00334713 Splice isoform 3 of Q99729 Heterogeneous nuclear ribonucleoprotein A/B Q99729 24.9 0.69 0.00 1.21
Proteins regulated upon paclitaxel treatment and induced N130 expression
IPI00168812 Transmembrane receptor PTK7-4 Q8NFA6 7 1.07 0.72 6.54
IPI00305185 Stromal cell protein Q9BRV3 10.9 2.37 1.67 3.15
IPI00328696 Hemoglobin alpha chain P01922 17 2.45 1.20 2.78
IPI00329705 KIAA1363 protein Q86WZ1 9.8 1.76 1.18 2.77
IPI00029079 GMP synthase [glutamine-hydrolyzing] P49915 4.5 1.33 2.70 2.50
IPI00298971 Vitronectin (Serum spreading factor) (S-protein) (V75) P04004 8.2 2.54 1.44 2.50
IPI00218816 beta globin P02023 19.7 1.81 2.10 2.49
IPI00052229 Hypothetical protein Q9UG74 6.7 0.68 0.98 2.48
IPI00160897 Hypothetical protein Q969E5 37.6 2.05 1.08 2.27
IPI00005202 Membrane associated progesterone receptor component 2 O15173 10.3 1.59 1.14 2.17
IPI00028481 Ras-related protein Rab-8 P24407 12.1 2.32 3.32 2.16
IPI00030131 Splice isoform Beta of P42167 Thymopoietin, isoforms beta/gamma P42167 14.1 1.81 1.23 2.11
IPI00016447 Hypothetical protein FLJ20502 Q9NX08 13.1 1.53 1.08 2.08
IPI00021785 Cytochrome c oxidase polypeptide Vb, mitochondrial P10606 25.6 1.31 1.38 2.03
IPI00010157 S-adenosylmethionine synthetase gamma form P31153 8.4 0.75 1.28 1.97
IPI00009407 Defender against cell death 1 P46966 14.2 1.39 1.13 1.94
IPI00026154 Protein kinase C substrate, 80 kDa protein, heavy chain P14314 10.2 1.21 0.78 1.90
IPI00021187 RuvB-like 1 Q9Y265 10.1 1.04 1.29 1.89
IPI00015786 Spectrin alpha chain, brain Q13813 11.2 1.71 1.41 1.89
IPI00395917 ferritin heavy chain P02794 14.8 1.44 0.87 1.87
IPI00236879 Ataxin-2 related domain protein Q8WWM7 7.1 1.74 1.51 1.84
IPI00377175 similar to Esterase D Q9BVJ2 6.9 0.89 1.08 1.78
IPI00032825 Hypothetical protein CGI-109 Q9Y3B3 6.5 1.14 1.04 1.77
IPI00016572 Small nuclear ribonucleoprotein G Q15357 17.1 1.60 1.52 1.77
IPI00027717 Component of gems 4 P57678 4.6 1.30 1.17 1.76
IPI00008453 Coronin 1C Q9ULV4 10.1 1.07 1.28 1.75
IPI00017726 Splice isoform 1 of Q99714 3- hydroxyacyl-CoA dehydrogenase type II Q99714 15.3 1.47 1.04 1.74
IPI00215916 cytochrome c P00001 29.5 1.61 1.21 1.73
IPI00219291 Splice isoform 2 of P56134 ATP synthase f chain, mitochondrial P56134 37.9 1.73 1.15 1.73
IPI00003927 40 kDa peptidyl-prolyl cis-trans isomerase Q08752 3.5 1.02 1.09 1.73
IPI00216172 Splice isoform LAMP-2B of P13473 Lysosome-associated P13473 4.9 1.29 1.18 1.73
IPI00107117 Peptidylprolyl isomerase B P23284 25.9 1.47 1.32 1.72
IPI00011274 JKTBP2 O14979 18.8 1.05 1.20 1.69
IPI00021439 Actin, cytoplasmic 1 P02570 59.5 1.62 1.50 1.68
IPI00009236 Caveolin-1 Q03135 24.2 1.56 1.22 1.66
IPI00305064 Splice isoform CD44 of P16070 CD44 antigen P16070 6.3 1.65 1.26 1.65
IPI00337814 Hypothetical protein Q9BWL4 11.7 1.37 1.16 1.65
IPI00003965 Ubiquitin carboxyl-terminal hydrolase 7 Q93009 1.9 1.40 1.08 1.65
IPI00219835 Splice isoform Alpha-S1 of P04895 Guanine nucleotide-binding protein G(S), P04895 23.9 1.55 1.06 1.64
IPI00295589 Eukaryotic translation initiation factor 4GI Q96I65 9.2 1.12 1.44 1.63
IPI00396589 Interleukin enhancer binding factor 2, 45kD Q9BWD4 19.2 2.01 0.88 1.63
IPI00010810 Electron transfer flavoprotein alpha-subunit, mitochondrial P13804 24.6 1.21 0.63 1.62
IPI00165092 Hypothetical protein FLJ13995 Q9H817 6.3 1.37 0.87 1.61
IPI00011229 Cathepsin D P07339 14.1 1.66 1.02 1.61
IPI00303882 Splice isoform B of O60664 Cargo selection protein TIP47 O60664 27.2 1.41 1.26 1.60
IPI00249267 similar to H2A histone family, member Z 39.1 1.05 0.91 1.57
IPI00025273 Splice isoform Long of P22102 Trifunctional purine biosynthetic protein adenosine-3 P22102 10.1 1.68 1.43 1.57
IPI00218019 Basigin long isoform Q8IZL7 9.6 1.59 0.84 1.57
IPI00385566 Hypothetical protein FLJ30014 Q969M3 3.5 1.50 1.62 1.56
IPI00385098 MSTP086 Q7Z4F2 7.1 0.83 0.99 1.54
IPI00027252 repressor of estrogen receptor activity Q9BXV3 15.1 1.31 0.96 1.54
IPI00291006 Malate dehydrogenase, mitochondrial P40926 26.9 1.28 1.00 1.54
IPI00031479 Protein disulfide isomerase A5 Q14554 2.3 1.43 0.88 1.54
IPI00395769 ATP synthase gamma chain, mitochondrial P36542 14 1.15 1.15 1.54
IPI00217952 Splice isoform 1 of Q06210 Q06210 8.6 1.51 0.96 1.52
IPI00329629 Glucosamine--fructose-6-phosphate DnaJ homolog subfamily C member 7 Q99615 5.3 0.86 0.72 1.50
IPI00003833 HSPC032 Q9Y6C9 16.8 0.83 1.26 1.49
IPI00297982 eukaryotic translation initiation factor 2, subunit 3 gamma, 52kDa P41091 21.8 1.20 1.20 1.49
IPI00220985 Keratin, type I cytoskeletal 18 P05783 39.6 1.46 0.94 1.49
IPI00025095 Cellular nucleic acid binding protein P20694 8.5 1.14 1.15 1.49
IPI00025874 Dolichyl-diphosphooligosaccharide--protein glycosyltransferase 67 kDa subunit P04843 15.7 1.65 1.20 1.48
IPI00142634 Tubulin beta-5 chain P05218 43 1.47 1.00 1.47
IPI00007188 ADP, ATP carrier protein, fibroblast isoform P05141 46.6 1.24 0.94 1.46
IPI00026167 NHP2-like protein 1 P55769 24.2 1.37 0.86 1.46
IPI00029557 GrpE protein homolog 1, mitochondrial Q9HAV7 18.4 1.65 1.30 1.46
IPI00164305 Membrane associated protein SLP-2 Q9UJZ1 11.8 1.24 1.10 1.45
IPI00293102 Splice isoform 2 of Q15257 Protein phosphatase 2A, regulatory subunit B′ Q15257 12.8 1.33 1.05 1.45
IPI00015148 Ras-related protein Rap-1b P09526 14.7 1.61 1.11 1.44
IPI00003519 116 kDa U5 small nuclear ribonucleoprotein component Q15029 7.2 1.17 0.83 1.44
IPI00176903 Leucine-zipper protein FKSG13 O00535 15.9 1.14 1.08 1.44
IPI00021954 Golgi-specific brefeldin A-resistance guanine nucleotide exchange factor 1 Q92538 7 1.37 0.87 1.43
IPI00015953 Nucleolar RNA helicase II Q9NR30 18.7 0.95 1.82 1.43
IPI00171626 hypothetical protein FLJ12443 Q7Z4G6 9.9 1.28 0.99 1.43
IPI00009922 DC50 Q9GZT3 12.8 1.33 1.01 1.42
IPI00220855 Similar to H2A histone family, member O Q9BTM1 58.9 1.33 0.89 1.42
IPI00220739 progesterone receptor membrane component 1 O00264 16.9 1.52 1.08 1.42
IPI00290142 CTP synthase P17812 12.5 1.33 1.10 1.42
IPI00297084 Dolichyl-diphosphooligosaccharide--protein glycosyltransferase 48 kDa subunit P39656 12.7 1.33 1.32 1.41
IPI00005270 Hypothetical protein Q9BVD2 1.8 1.32 2.46 1.41
IPI00019927 26S proteasome non-ATPase regulatory subunit 7 P51665 11.4 1.25 1.17 1.41
IPI00333010 SR-related CTD associated factor 6 Q8WU30 8 1.18 1.03 1.40
IPI00028091 Actin-like protein 3 P32391 6.7 0.80 1.16 1.40
IPI00386685 citrate synthase isoform a Q96FZ8 17 1.24 1.06 1.40
IPI00007824 ABP125 Q9UM06 4.2 1.08 0.78 1.40
IPI00219219 beta-galactosidase binding lectin P09382 65.2 1.81 1.14 1.39
IPI00021440 Actin, cytoplasmic 2 P02571 59.5 1.27 0.48 1.39
IPI00024919 Thioredoxin-dependent peroxide reductase, mitochondrial P30048 25.4 1.40 1.05 1.39
IPI00020984 Calnexin P27824 17.1 1.29 1.09 1.38
IPI00386803 LIM and SH3 protein 1 Q96IG0 10.2 1.16 1.02 1.38
IPI00012578 Importin alpha-4 subunit O00629 13.4 1.24 0.96 1.37
IPI00215918 ADP-ribosylation factor 4 P18085 22.2 1.17 0.81 1.37
IPI00019345 Ras-related protein Rap-1A P10113 14.7 1.21 0.75 1.37
IPI00023542 gp25L2 protein Q9BVK6 20.9 1.23 1.12 1.37
IPI00009328 Probable ATP-dependent helicase DDX48 P38919 15.8 1.10 1.01 1.37
IPI00291467 ADP, ATP carrier protein, liver isoform T2 P12236 44.3 1.29 1.00 1.36
IPI00219604 mitogen-activated protein kinase kinase 1 Q02750 7.6 1.39 0.99 1.36
IPI00009342 Ras GTPase-activating-like protein IQGAP1 P46940 11.9 1.08 0.95 1.36
IPI00027493 4F2 cell-surface antigen heavy chain P08195 22.9 1.37 0.97 1.36
IPI00333383 Adapter-related protein complex 2 beta 1 subunit P21851 2.8 1.13 1.09 1.36
IPI00148062 Nuclear-associated protein SPAN-Xb Q9NS25 43.7 1.25 1.42 1.36
IPI00396304 tubulin, alpha, ubiquitous P68363 39.7 1.27 0.88 1.36
IPI00034283 Similar to tubulin, beta, 4 Q9BUF5 34.8 1.07 1.12 1.35
IPI00018206 Aspartate aminotransferase, mitochondrial P00505 8.6 1.00 0.79 1.35
IPI00002134 26S proteasome non-ATPase regulatory subunit 5 Q16401 19.4 1.23 1.25 1.35
IPI00022793 Trifunctional enzyme beta subunit, mitochondrial (TP- beta) P55084 13.5 1.30 1.29 1.35
IPI00216492 Splice isoform 2 of P31942 Heterogeneous nuclear ribonucleoprotein H3 P31942 8.2 1.55 1.14 1.35
IPI00002520 Serine hydroxymethyltransferase, mitochondrial P34897 12.5 1.79 1.13 1.34
IPI00221012 Splice isoform Long of Q93008 Probable ubiquitin carboxyl-terminal hydrolase FAF-X Q93008 6.1 1.11 1.02 1.34
IPI00217466 H1 histone family, member 3 P16402 29 1.18 0.98 1.34
IPI00216312 vimentin P08670 53.4 1.25 1.15 1.34
IPI00215914 ADP-ribosylation factor 1 P32889 47.5 1.00 0.89 1.33
IPI00046828 similar to CG15881-PB Q4VC31 14.5 1.37 1.33
IPI00026111 Hypothetical protein Q9BZS3 14.3 1.15 1.03 1.33
IPI00016638 ATP synthase alpha chain, mitochondrial P25705 18.1 1.21 0.95 1.33
IPI00215920 ADP-ribosylation factor 6 P26438 10.9 1.00 1.13 1.33
IPI00218889 Splice isoform 2 of P50570 Dynamin 2 P50570 8.7 1.23 0.95 1.33
IPI00218343 Tubulin alpha-6 chain Q9BQE3 59.8 1.25 0.95 1.33
IPI00006482 Splice isoform Long of P05023 P05023 10.8 1.31 1.31 1.33
IPI00165164 Sodium/Similar to ubiquitin-conjugating enzyme E2I Q9BQ25 16.8 0.67 0.90 1.32
IPI00185600 Annexin A11 P50995 8.3 0.85 0.74 1.32
IPI00021766 Splice isoform 1 of Q9NQC3 Reticulon 4 Q9NQC3 7.1 1.41 1.09 1.32
IPI00028055 Transmembrane protein Tmp21 P49755 22.4 1.41 1.11 1.32
IPI00026328 Thioredoxin-like protein p19 O95881 5.2 1.33 1.30 1.32
IPI00220362 10 kDa heat shock protein, mitochondrial Q04984 14.2 1.14 1.07 1.32
IPI00008708 PBK1 protein Q8WUZ1 8.3 1.00 1.07 1.32
IPI00306667 Splice isoform CNPII of P09543 2′,3′-cyclic nucleotide 3′-phosphodiesterase P09543 4.1 0.99 1.13 1.31
IPI00329351 60 kDa heat shock protein, mitochondrial P10809 31 1.33 1.05 1.31
IPI00027230 Endoplasmin P14625 22.4 1.21 1.04 1.31
IPI00004968 Nuclear matrix protein NMP200 Q9UMS4 6.9 1.20 0.98 1.31
IPI00396417 MHC class I antigen Q861B7 6.3 0.57 1.20 1.31
IPI00215884 splicing factor, arginine/serine-rich 1 (splicing factor 2, alternate splicing factor) Q07955 27 1.18 1.11 1.30
IPI00186338 unnamed protein 41.9 1.33 1.09 1.30
IPI00374260 ribosomal protein L10 P27635 30.4 0.68 0.98 0.69
IPI00296635 1,4-alpha-glucan branching enzyme Q04446 2.5 0.52 0.97 0.69
IPI00027681 Nicotinamide N-methyltransferase P40261 13.3 0.82 0.89 0.69
IPI00217468 H1 histone family, member 5 P16401 21.2 0.98 1.33 0.68
IPI00021840 40S ribosomal protein S6 P10660 23.3 0.75 1.04 0.68
IPI00017292 Splice isoform 1 of P35222 Beta-catenin P35222 9.1 1.09 0.68 0.68
IPI00021383 Heterogeneous nuclear ribonucleoprotein A3 P51991 19 1.03 1.58 0.67
IPI00386590 DJ423B22.4 (Ribosomal protein S27 Q9BQZ7 13.1 0.90 1.19 0.66
IPI00382700 Splice isoform 6 of O75369 Filamin B O75369 7.1 0.55 0.90 0.66
IPI00396660 Elongation factor 1-beta P24534 17.4 0.93 1.17 0.66
IPI00014808 Platelet-activating factor acetylhydrolase IB gamma subunit Q15102 27.3 0.92 0.93 0.66
IPI00219757 Glutathione S-transferase P P09211 31.5 0.73 0.92 0.65
IPI00186712 40S ribosomal protein S26 P02383 20.6 0.65 1.20 0.65
IPI00015952 Eukaryotic translation initiation factor 4 gamma 2 P78344 5.6 0.71 1.13 0.65
IPI00216320 Splice isoform 2 of O00764 Pyridoxal kinase O00764 14.8 0.88 0.93 0.64
IPI00217223 Multifunctional protein ADE2 [Includes: P22234 9.8 0.74 0.75 0.64
IPI00025019 Proteasome subunit beta type 1 P20618 15.8 1.52 1.00 0.64
IPI00386491 Splice isoform Short of Q00839 Heterogenous nuclear ribonucleoprotein U Q00839 21 0.72 0.94 0.64
IPI00008918 Splice isoform Beta of Q9UHB6 Epithelial protein lost in neoplasm Q9UHB6 7.2 0.98 1.52 0.64
IPI00332371 6-phosphofructokinase, liver type P17858 16.5 0.70 0.81 0.63
IPI00219520 Splice isoform Short of O75534 UNR protein O75534 5.6 0.66 1.03 0.63
IPI00033904 similar to ribosomal protein S3a P61247 26.5 0.69 1.03 0.63
IPI00013184 N-terminal acetyltransferase complex ARD1 subunit homolog P41227 4.7 1.13 1.15 0.63
IPI00336008 aldehyde dehydrogenase 5A1 isoform 1 Q8N3W7 8.2 1.22 1.12 0.62
IPI00215790 60S ribosomal protein L38 P23411 19.3 0.65 0.93 0.62
IPI00000874 Peroxiredoxin 1 Q06830 41.2 0.81 1.00 0.62
IPI00018219 Transforming growth factor-beta induced protein IG-H3 Q15582 7.2 0.96 0.81 0.60
IPI00032826 Hsc70-interacting protein P50502 13 0.65 1.02 0.60
IPI00163230 COP9 signalosome subunit 6 O15387 12.8 0.52 0.59 0.60
IPI00000051 Prefoldin subunit 1 O60925 23.8 0.81 1.17 0.59
IPI00259901 similar to peptidylprolyl isomerase A (cyclophilin A) Q68J44 8.5 0.88 2.07 0.59
IPI00027463 Calcyclin P06703 24.4 0.66 1.11 0.58
IPI00374119 smooth muscle and non-muscle myosin alkali light chain isoform 3 33.8 0.72 1.09 0.58
IPI00025512 Heat shock 27 kDa protein P04792 38.5 0.73 0.87 0.57
IPI00302850 Small nuclear ribonucleoprotein Sm D1 P13641 31.5 0.84 0.85 0.57
IPI00008524 Polyadenylate-binding protein 1 P11940 23.3 0.66 0.97 0.56
IPI00219486 Splice isoform 2 of P16632 40S ribosomal protein S24 P16632 9.2 0.52 1.05 0.56
IPI00004656 Beta-2-microglobulin P01884 21.8 0.84 0.73 0.55
IPI00215719 60S ribosomal protein L18 Q07020 31 0.67 1.10 0.55
IPI00219446 prostatic binding protein P30086 20.3 0.68 0.43 0.54
IPI00219158 ribosomal protein L29 P47914 30.8 0.69 1.17 0.53
IPI00375511 Similar to RIKEN cDNA 2510008H07 gene Q8N6E1 21.5 0.77 1.15 0.50
IPI00000861 LIM and SH3 domain protein 1 Q14847 9.2 0.64 0.71 0.48
IPI00026271 40S ribosomal protein S14 P06366 24.5 0.73 1.13 0.47
IPI00011603 26S proteasome non-ATPase regulatory subunit 3 O43242 8.6 0.60 1.01 0.37
IPI00165486 similar to ribosomal protein S2 9.6 0.63 0.93 0.28
IPI00021700 Proliferating cell nuclear antigen P12004 8.8 0.72 1.26 0.19
IPI00385399 mitogen-activated protein kinase 3 P27361 18.7 0.05 0.06 0.11
IPI00218547 Delta 1-pyrroline-5-carboxylate synthetase (P5CS) P54886 4.7 0.00 0.82 0.00

Although the proteins identified in this proteomic study need further investigation to facilitate the understanding of the biological mechanism of Nac1 function or paclitaxel treatment, the results provide a list of proteins and cellular machinery, including ribosomal complexes, cell surface antigens, and stress response proteins, such as heat shock proteins and acute-phase proteins. These protein changes associated with Nac1 or paclitaxel resistance can be exploited as targets for treatment of paclitaxel resistance.

To further analyze the relationship of the protein changes upon paclitaxel treatment, the GO categories of protein changes were classified. Cellular components analysis revealed that the protein changes are significantly overrepresented in mitochondrion in the set of all proteins identified by iTRAQ (p value of Fisher’s product: 8.8 e-5, p value corrected by multiple testing: 0.024).

Furthermore, we found some interesting co-regulation of tubulin and mitochondrial proteins after paclitaxel treatment. Tubulin is a well-known target for paclitaxel function and responsible for paclitaxel induced cell death [33]. One of the mechanisms of paclitaxel function is believed to induce cell death by altering microtubule assembly through the binding to the microtubule polymer so as to stabilize microtubules [34], as a result, it disrupts the normal re-assembling of microtubule network which is required by mitosis and cell proliferation [3]. Another protein, cytochrome c, was reported previously of release from mitochondrion thus inducing cell apoptosis upon paclitaxel treatment [35]. However, how cytochrome c was released upon paclitaxel treatment is not clear. Interestingly, in this study, both α-4 and β-5 subunit of tubulin were observed of up-regulated after paclitaxel treatment (Table 1), so were many mitochondrial proteins including mitochondrial trifunctional enzyme, mitochondrial ATP synthase, cytochrome c, Serine hydroymethyltransferase, GrpE protein homolog 1, Mitochondrial inner membrane protein, Complement component 1 Q subcomponent binding protein, Thioredoin-dependent peroide reductase, and mitochondrial malate dehydrogenase, etc.

This observation suggests a regulation of mitochondrial function associated with paclitaxel treatment and tubulins. The regulation of mitochondrial function by tubulins was also reported by several studies recently. The regulation might be the result of direct interaction of the voltage-dependent anion channel (VDAC) on mitochondrial outer membrane with tubulin [3638]. Taken together, a hypothesis is that mitochondria may be involved in the response to paclitaxel treatment. Mitochondrial function is the key player for cell apoptosis, and the mechanism of paclitaxel treatment might be to induce apoptosis through tubulin polymerization and regulation of mitochondrial function.

3.4 protein changes determined by label-free quantitation

Quantitative analysis using different quantitative proteomic methods may increase the confidence of the protein changes if the proteins could be identified and quantified by multiple methods consistently. To this end, label free quantitation methods were also employed in this study. The tryptic peptides from the four cell states without iTRAQ labeling were analyzed three times with the QSTAR for the LC-MS quantitative analysis and two times with the LTQ for spectral count (Figure 1). A total of 383 proteins were quantified by the LC-MS method, and 757 were quantified by spectral count (Figure 4).

Figure 4.

Figure 4

Venn diagram depicts the number of proteins quantified by each quantitation method

We then determined the proteins that were changed in two cell states quantified by LC-MS and spectral count. Similar as iTRAQ quantitation, proteins that fell out of one standard deviation of the normal distribution curve were considered as with changed expression. The thresholds were determined as <0.75 and >1.15 for both spectral count and LC-MS.

Since N130 was induced expressed in cells, N130 was the perfect internal control for quantitation. N130 should be over-expressed in N130 ON cells compared to the N130 OFF cells. All the three quantitation showed the higher abundance of N130 in N130 ON cells (Table 1). However, the detection limitation varied among the three methods. The N130 overexpression were undetectable in N130 OFF cells for LC-MS and spectral count (Table 1), which may come from different instrumentations with various dynamic range and background. Further work and experiments will help define the most accurate quantifications along with better standards for calibrating the ratio of protein abundance from different methods. This is needed in proteomics to improve quantitative accuracy [28]. Nevertheless, the quantitative results of N130 expression confirmed that the three quantitative proteomic methods could be used to increase the confidence of quantitation.

For the mitochondria protein changes upon piclitaxel treatment, 7 out of 14 proteins determined by iTRAQ were also measured by the label free methods in the same track, e.g. ATP synthase, cytochrome c, Trifunctional enzyme, and Enoyl-CoA hydratase, etc (Table 3). Those proteins consistently determined by the two label-free methods confirmed the real changes of mitochondrial proteins upon piclitaxel treatment. This study represents the first proteomic study to discover the association of paclitaxel treatment and mitochondria protein changes in ovarian cancer cells, which may offer a new direction for studying the mechanism of drug resistance of cancer cells.

Table 3.

Mitochondrial protein changes related to paclitaxel quantified by iTRAQ were also measured by label-free quantitation methods

IPI ProteinName Swiss-Prot methods OFF+T/OFF−T ON−T/OFF−T ON+T/OFF−T
IPI00002520 Serine hydroxymethyltransferase, mitochondrial P34897 iTRAQ 1.79 1.13 1.34
IPI00219291 ATP synthase f chain, mitochondrial P56134 iTRAQ 1.73 1.15 1.73
LC-MS 1.70 1.16 1.99
IPI00029557 GrpE protein homolog 1, mitochondrial Q9HAV7 iTRAQ 1.65 1.30 1.46
IPI00009960 Mitochondrial inner membrane protein Q16891 iTRAQ 1.64 1.20 1.05
IPI00215916 cytochrome c P00001 iTRAQ 1.61 1.21 1.73
LC-MS 1.19 1.00 1.27
IPI00031522 Trifunctional enzyme alpha subunit, mitochondrial P40939 iTRAQ 1.55 0.80 1.19
SC 3.01 3.00 6.06
IPI00014230 Complement component 1, mitochondrial Q07021 iTRAQ 1.47 1.17 1.25
SC 1.59 0.60 0.40
IPI00024919 Thioredoxin-dependent peroxide reductase, mitochondrial P30048 iTRAQ 1.40 1.05 1.39
SC 1.25 0.50 1.00
IPI00024993 Enoyl-CoA hydratase, mitochondrial P30084 iTRAQ 1.38 0.81 1.12
SC 1.67 1.33 1.33
IPI00329351 60 kDa heat shock protein, mitochondrial P10809 iTRAQ 1.33 1.05 1.31
IPI00007765 Stress-70 protein, mitochondrial P38646 iTRAQ 1.33 1.05 1.12
LC-MS 1.71 1.21 1.47
SC 1.75 1.50 1.65
IPI00021785 Cytochrome c oxidase polypeptide Vb, mitochondrial P10606 iTRAQ 1.31 1.38 2.03
IPI00304802 Dihydrolipoamide succinyltransferase component of 2-oxoglutarate dehydrogenase complex, mitochondrial P36957 iTRAQ 1.31 0.92 1.23
IPI00022793 Trifunctional enzyme beta subunit, mitochondrial P55084 iTRAQ 1.30 1.29 1.35

4 Concluding remarks

In this study, 1371 proteins were identified and quantified from Nac1 dominant negative model, SKOV-3 N130 cell line, associated with paclitaxel resistance and Nac1 function using iTRAQ quantitation, LC-MS method and spectral count. Candidate proteins related to paclitaxel resistance and NAC1 function were determined. Go analysis of the protein changes upon paclitaxel resistance revealed that protein changes significantly overrepresented in mitochondria. The co-regulation of tubulins and mitochondrial proteins was found, which suggests the roles of mitochondria in response to paclitaxel treatment. The identified proteins will be useful for further study of biological functions of Nac1 and elucidation of the molecular mechanism of paclitaxel treatment and resistance.

Supplementary Material

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Acknowledgments

This work was supported by federal funds from the National Cancer Institute, National Institutes of Health, by grant R21-CA-114852 and RO1-CA-103937 (IMS) and Early Detection and Research Network (EDRN). We gratefully acknowledge the support from the Mass Spectrometry Facility at the Johns Hopkins University and the support from Applied Biosystems.

Footnotes

The authors have declared no conflict of interest.

References

  • 1.Khayat D, Antoine EC, Coeffic D. Taxol in the management of cancers of the breast and the ovary. Cancer Invest. 2000;18:242–260. doi: 10.3109/07357900009031828. [DOI] [PubMed] [Google Scholar]
  • 2.Jordan MA, Wendell K, Gardiner S, Derry WB, et al. Mitotic block induced in HeLa cells by low concentrations of paclitaxel (Taxol) results in abnormal mitotic exit and apoptotic cell death. Cancer Res. 1996;56:816–825. [PubMed] [Google Scholar]
  • 3.Amos LA, Lowe J. How Taxol stabilises microtubule structure. Chem Biol. 1999;6:R65–69. doi: 10.1016/s1074-5521(99)89002-4. [DOI] [PubMed] [Google Scholar]
  • 4.Sangrajrang S, Fellous A. Taxol resistance. Chemotherapy. 2000;46:327–334. doi: 10.1159/000007306. [DOI] [PubMed] [Google Scholar]
  • 5.Ishibashi M, Nakayama K, Yeasmin S, Katagiri A, et al. A BTB/POZ gene, NAC-1, a tumor recurrence-associated gene, as a potential target for Taxol resistance in ovarian cancer. Clin Cancer Res. 2008;14:3149–3155. doi: 10.1158/1078-0432.CCR-07-4358. [DOI] [PubMed] [Google Scholar]
  • 6.Nakayama K, Nakayama N, Davidson B, Sheu JJ, et al. A BTB/POZ protein, NAC-1, is related to tumor recurrence and is essential for tumor growth and survival. Proc Natl Acad Sci U S A. 2006;103:18739–18744. doi: 10.1073/pnas.0604083103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Zhang H, Li XJ, Martin DB, Aebersold R. Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat Biotechnol. 2003;21:660–666. doi: 10.1038/nbt827. [DOI] [PubMed] [Google Scholar]
  • 8.Gygi SP, Rist B, Gerber SA, Turecek F, et al. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol. 1999;17:994–999. doi: 10.1038/13690. [DOI] [PubMed] [Google Scholar]
  • 9.Ross PL, Huang YN, Marchese JN, Williamson B, et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics. 2004;3:1154–1169. doi: 10.1074/mcp.M400129-MCP200. [DOI] [PubMed] [Google Scholar]
  • 10.Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics. 2002;1:376–386. doi: 10.1074/mcp.m200025-mcp200. [DOI] [PubMed] [Google Scholar]
  • 11.Yao X, Freas A, Ramirez J, Demirev PA, Fenselau C. Proteolytic 18O labeling for comparative proteomics: model studies with two serotypes of adenovirus. Anal Chem. 2001;73:2836–2842. doi: 10.1021/ac001404c. [DOI] [PubMed] [Google Scholar]
  • 12.Bondarenko PV, Chelius D, Shaler TA. Identification and relative quantitation of protein mixtures by enzymatic digestion followed by capillary reversed-phase liquid chromatography-tandem mass spectrometry. Anal Chem. 2002;74:4741–4749. doi: 10.1021/ac0256991. [DOI] [PubMed] [Google Scholar]
  • 13.Chelius D, Bondarenko PV. Quantitative profiling of proteins in complex mixtures using liquid chromatography and mass spectrometry. J Proteome Res. 2002;1:317–323. doi: 10.1021/pr025517j. [DOI] [PubMed] [Google Scholar]
  • 14.Liu H, Sadygov RG, Yates JR., 3rd A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem. 2004;76:4193–4201. doi: 10.1021/ac0498563. [DOI] [PubMed] [Google Scholar]
  • 15.Zhang H, Yi EC, Li XJ, Mallick P, et al. High throughput quantitative analysis of serum proteins using glycopeptide capture and liquid chromatography mass spectrometry. Mol Cell Proteomics. 2005;4:144–155. doi: 10.1074/mcp.M400090-MCP200. [DOI] [PubMed] [Google Scholar]
  • 16.Olson MT, Blank PS, Sackett DL, Yergey AL. Evaluating reproducibility and similarity of mass and intensity data in complex spectra--applications to tubulin. J Am Soc Mass Spectrom. 2008;19:367–374. doi: 10.1016/j.jasms.2007.11.012. [DOI] [PubMed] [Google Scholar]
  • 17.Frewen BE, Merrihew GE, Wu CC, Noble WS, MacCoss MJ. Analysis of peptide MS/MS spectra from large-scale proteomics experiments using spectrum libraries. Anal Chem. 2006;78:5678–5684. doi: 10.1021/ac060279n. [DOI] [PubMed] [Google Scholar]
  • 18.Lam H, Deutsch EW, Eddes JS, Eng JK, et al. Development and validation of a spectral library searching method for peptide identification from MS/MS. Proteomics. 2007;7:655–667. doi: 10.1002/pmic.200600625. [DOI] [PubMed] [Google Scholar]
  • 19.Shilov IV, Seymour SL, Patel AA, Loboda A, et al. The Paragon Algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra. Mol Cell Proteomics. 2007;6:1638–1655. doi: 10.1074/mcp.T600050-MCP200. [DOI] [PubMed] [Google Scholar]
  • 20.Eng JM, AL, Yates JR., 3rd An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom. 1994;5:13. doi: 10.1016/1044-0305(94)80016-2. [DOI] [PubMed] [Google Scholar]
  • 21.Han DK, Eng J, Zhou H, Aebersold R. Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry. Nat Biotechnol. 2001;19:946–951. doi: 10.1038/nbt1001-946. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Keller A, Nesvizhskii AI, Kolker E, Aebersold R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem. 2002;74:5383–5392. doi: 10.1021/ac025747h. [DOI] [PubMed] [Google Scholar]
  • 23.Li XJ, Yi EC, Kemp CJ, Zhang H, Aebersold R. A software suite for the generation and comparison of peptide arrays from sets of data collected by liquid chromatography-mass spectrometry. Mol Cell Proteomics. 2005;4:1328–1340. doi: 10.1074/mcp.M500141-MCP200. [DOI] [PubMed] [Google Scholar]
  • 24.Ashburner M, Ball CA, Blake JA, Botstein D, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–29. doi: 10.1038/75556. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Westfall PaYS. Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment. 1993. [Google Scholar]
  • 26.Nakayama K, Nakayama N, Wang TL, Shih Ie M. NAC-1 controls cell growth and survival by repressing transcription of Gadd45GIP1, a candidate tumor suppressor. Cancer Res. 2007;67:8058–8064. doi: 10.1158/0008-5472.CAN-07-1357. [DOI] [PubMed] [Google Scholar]
  • 27.Li XJ, Zhang H, Ranish JA, Aebersold R. Automated statistical analysis of protein abundance ratios from data generated by stable-isotope dilution and tandem mass spectrometry. Anal Chem. 2003;75:6648–6657. doi: 10.1021/ac034633i. [DOI] [PubMed] [Google Scholar]
  • 28.Lau KW, Jones AR, Swainston N, Siepen JA, Hubbard SJ. Capture and analysis of quantitative proteomic data. Proteomics. 2007;7:2787–2799. doi: 10.1002/pmic.200700127. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Li Y, Sokoll LJ, Rush J, Zou N, Chan DW. Targeted detection of prostate cancer proteins in serum using heavy peptide standards and MALDI-TOF/TOF. Proteomics-Clinical Applications. 2009 In press. [Google Scholar]
  • 30.Stahl-Zeng J, Lange V, Ossola R, Eckhardt K, et al. High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites. Mol Cell Proteomics. 2007;6:1809–1817. doi: 10.1074/mcp.M700132-MCP200. [DOI] [PubMed] [Google Scholar]
  • 31.Keshishian H, Addona T, Burgess M, Kuhn E, Carr SA. Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution. Mol Cell Proteomics. 2007;6:2212–2229. doi: 10.1074/mcp.M700354-MCP200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Anderson L, Hunter CL. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol Cell Proteomics. 2006;5:573–588. doi: 10.1074/mcp.M500331-MCP200. [DOI] [PubMed] [Google Scholar]
  • 33.Umezu T, Shibata K, Kajiyama H, Terauchi M, et al. Taxol resistance among the different histological subtypes of ovarian cancer may be associated with the expression of class III beta-tubulin. Int J Gynecol Pathol. 2008;27:207–212. doi: 10.1097/PGP.0b013e318156c838. [DOI] [PubMed] [Google Scholar]
  • 34.Orr GA, Verdier-Pinard P, McDaid H, Horwitz SB. Mechanisms of Taxol resistance related to microtubules. Oncogene. 2003;22:7280–7295. doi: 10.1038/sj.onc.1206934. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Kuo HC, Lee HJ, Hu CC, Shun HI, Tseng TH. Enhancement of esculetin on Taxol-induced apoptosis in human hepatoma HepG2 cells. Toxicol Appl Pharmacol. 2006;210:55–62. doi: 10.1016/j.taap.2005.06.020. [DOI] [PubMed] [Google Scholar]
  • 36.Rostovtseva TK, Sheldon KL, Hassanzadeh E, Monge C, et al. Tubulin binding blocks mitochondrial voltage-dependent anion channel and regulates respiration. Proc Natl Acad Sci U S A. 2008;105:18746–18751. doi: 10.1073/pnas.0806303105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Carre M, Andre N, Carles G, Borghi H, et al. Tubulin is an inherent component of mitochondrial membranes that interacts with the voltage-dependent anion channel. J Biol Chem. 2002;277:33664–33669. doi: 10.1074/jbc.M203834200. [DOI] [PubMed] [Google Scholar]
  • 38.Rostovtseva TK, Bezrukov SM. VDAC regulation: role of cytosolic proteins and mitochondrial lipids. J Bioenerg Biomembr. 2008;40:163–170. doi: 10.1007/s10863-008-9145-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

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