Proteomic Analysis of Propiconazole Responses in Mouse Liver: Comparison of Genomic and Proteomic Profiles Pedro A. Ortiz, Maribel E. Bruno, Tanya Moore, Stephen Nesnow, Witold Winnik, and Yue Ge* National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711 Received August 25, 2009
We have performed for the first time a comprehensive profiling of changes in protein expression of soluble proteins in livers from mice treated with the mouse liver tumorigen, propiconazole, to uncover the pathways and networks altered by this fungicide. Utilizing two-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS), we identified 62 proteins that were altered. Several of these protein changes detected by 2-DE/MS were verified by Western blot analyses. These differentially expressed proteins were mapped using Ingenuity Pathway Analyses (IPA) canonical pathways and IPA tox lists. Forty-four pathways/lists were identified. IPA was also used to create networks of interacting protein clusters. The protein-generated IPA canonical pathways and IPA tox lists were compared to those pathways and lists previously generated from genomic analyses from livers of mice treated with propiconazole under the same experimental conditions. There was a significant overlap in the specific pathways and lists generated from the proteomic and the genomic data with 27 pathways common to both proteomic and genomic analyses. However, there were also 17 pathways/lists identified only by proteomics analysis and 21 pathways/lists only identified by genomic analysis. The protein network analysis produced interacting subnetworks centered around hepatocyte nuclear factor 4R (HNF4R), MYC, proteasome subunit type 4R, and glutathione S-transferase (GST). The HNF4R network hub was also identified by genomic analysis. Five GST isoforms were identified by proteomic analysis and GSTs were present in 10 of the 44 protein-based pathways/lists. Hepatic GST activities were compared between mice treated with propiconazole and 2 additional conazoles and higher GST activities were found to be associated with the tumorigenic conazoles. Overall, this comparative proteomic and genomic study has revealed a series of alterations in livers induced by propiconazole: nuclear receptor activation, metabolism of xenobiotics, metabolism of biochemical intermediates, biosynthesis of biochemical intermediates, and oxidative stress in mouse liver. The present study provides novel insights into toxic mechanisms and/or modes of action of propiconazole which are required for human health risk assessment of this environmental chemical. Keywords: proteomic • propiconazole • protein expression • mouse • networks • pathways
Introduction
increases in mutant frequencies in the livers of male C57BL/6 Big Blue mice.10
Propiconazole, a systemic fungicide used for foliar disease prevention on fruits, vegetables, grasses and seeds,1 is hepatotoxic and hepatotumorigenic in mice,2,3 and is classified as a possible human carcinogen.3 In mice, propiconazole has been shown to induce hepatocellular hypertrophy and cell proliferation,4 to increase cytochrome-P450 (CYP) protein levels and enzymatic activities,4-6 alter gene expression,7,8 induce reactive oxygen species (ROS) and oxidize cellular proteins,9 reduce serum cholesterol levels4 and hepatic retinoic acid levels.5 Propiconazole has also been found to induce significant
In rodents, liver is a common target for environmental pesticides and liver cancer is frequently diagnosed as a toxic end point.11 Because of its importance in normal and pathological development, liver has been the major subject of investigation since the emergence of proteomic technologies in the early 1990s.12,13 A variety of 2-DE based proteomics studies have been conducted that describe the quantitative level of liver proteins under basal and pathophysiological conditions.12,13 A number of studies have been performed that describe changes in protein expression in hepatic cells or liver tissues treated with various environmental toxicants including carcinogens.14,15 Utilizing 2-DE, immunoblotting, and MS, we have previously identified 17 carbonylated proteins that were altered with varying intensities in the livers of mice treated with propiconazole.9 Glycolysis, mitochondrial respiratory chain,
* Corresponding author: Yue Ge, Ph.D., Integrated Systems Toxicology Division, B 143-06, National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711 USA. Telephone: 919-541-2202. Fax: 919-541-0694. E-mail:
[email protected].
1268 Journal of Proteome Research 2010, 9, 1268–1278 Published on Web 01/22/2010
10.1021/pr900755q
This article not subject to U.S. Copyright. Published 2010 by the American Chemical Society
Proteomic Analysis of Propiconazole Responses ATP production, amino acid metabolism, CO2 hydration and cellular antioxidant defense and detoxification systems were affected by oxygen radicals in the livers of propiconazoletreated mice.9 We have recently completed a comparative transcriptomic evaluation of several mouse liver nontumorigenic and tumorigenic conazoles including propiconazole.7 In these analyses, we compared results for the myclobutanil (nontumorigen), triadimefon and propiconazole (tumorigens) by mapping their responses to Ingenuity Pathway Analysis (IPA) tox lists and IPA canonical pathways, and Gene-Go MetaCore dynamic networks and their central hubs. These studies provided important clues to the contribution of these pathways and networks to liver toxicity and cancer, and helped identify critical toxicity pathways induced by propiconazole. In the current study, we performed, for the first time, a comprehensive protein expression or proteomic analysis of the soluble fraction of liver from mice treated with propiconazole (2500 ppm) in the diet for 4 days. These tissues from our earlier study were obtained using the same experimental conditions used to generate samples previously used in transcriptomic analyses. The current approach using proteomic analysis focused on the identification of potential early toxicity pathways and networks associated with propiconazole treatment. In this study, approximately 3000 protein spots were separated by 2-DE and 62 unique differentially expressed proteins were identified by MS. We compared these proteomic results with our previous transcriptomic findings to further our understanding of the toxic or carcinogenic processes induced by propiconazole at the protein level. We found many similarities in the differentially expressed proteins and differentially expressed mRNA transcripts altered by propiconazole treatment. The pathways affected at both the mRNA and the protein levels suggest the importance of these pathways in propiconazoleinduced toxicity. In addition, we identified a number of new pathways and networks previously not identified by transcriptomic analyses. The abundance of GSTs in the differentially expressed proteins and in the canonical pathways, tox lists and networks prompted a comparison of GST activities altered by a series of conazoles.
Materials and Methods Materials. Propiconazole was a gift from Syngenta Crop Protection, Inc. (Greensboro, NC). Myclobutanil and triadimefon were obtained from LKT Laboratories (St. Paul, MN). Trypsin Gold, mass spectrometry grade was obtained from Promega (Madison, WI). Dithiothreitol (DTT) was purchased from Bioanalytical (Natick, MA). Cydyes (Cy3, Cy2 and Cy5), Typhoon 9410 scanner, Image Quant software, nonlinear IPG strips (pH 3-11), Ettan IPGPhor apparatus, Decyder software, and plus bind silane were manufactured by GE Healthcare (Piscataway, NJ). Methanol (HPLC grade) and glacial acetic acid were purchased from Fisher Scientific (Fair Lawn, NJ). The LXQ linear ion trap mass spectrometer, LC surveyor system with autosampler, LC pumps and Bioworks software were manufactured by Thermo Fisher Scientific (Waltham, MA). Protean II apparatus and 40% bis acrylamide gel solution were the products from Bio-Rad (Hercules, CA). EZQ protein quantitation kit was purchased from Invitrogen, Inc. (Carlsbad, CA). Anti-GST mu and alpha antibodies were obtained from Detroit R & D, Inc. (Detroit, MI). IPA Software was a product from Ingenuity Systems (Redwood City, CA). GST activity assay kit and all other chemicals were obtained from Sigma-Aldrich (St. Louis, MO).
research articles Conazole Treatments. The complete details of the in-life experiments of the treatment of mice with the conazoles have been previously described.7 Briefly, 5-6 week old male CD-1 mice were obtained from Charles River Laboratories (Raleigh, NC). The mice were housed with access to feed (Purina 5001 Rodent Diet) and water ad libitum in the animal facility with a 12-h light/dark cycle under controlled temperature (20-22 °C) and humidity (40-60%). All animals were acclimated for 1 week and then randomly assigned to the treatment groups. Groups of mice were fed Purina 5001 Rodent Diet (control) or diets of feed containing propiconazole (2500 ppm), triadimefon (1800 ppm), or myclobutanil (2000 ppm) for 4 days.7 At the end of treatment, their body weights were recorded and mice were euthanized via carbon dioxide asphyxiation. Livers were immediately removed, rinsed with cold 1.15% KCl/0.25 M sucrose, weighed, minced, and immediately frozen in liquid nitrogen. The frozen tissues were transferred into a -80 °C freezer until use. All aspects of the study were conducted in facilities accredited by the Association for the Assessment and Accreditation of Laboratory Animal Care, International (AAALAC, Int.), and all procedures involving the use of animals were approved by the NHEERL Institutional Animal Care and Use Committee. Isolation of Soluble Proteins from Mouse Livers for 2-DE Electrophoresis. Two grams of each liver sample was minced into small pieces and homogenized in 15 mL of cold sucrose solution (0.25 M) using Dounce apparatus. The homogenized mixtures were then centrifuged at 9000g for 20 min at 4 °C. The supernatants were transferred to centrifuge tubes and centrifuged at 105 000g for 60 min at 4 °C. The supernatant obtained was the soluble fraction used for the 2-DE analysis. Glutathione S-transferase Activity Assay. Two hundred and fifty milligrams of liver tissue was placed in prechilled glass tubes and homogenized in 2 mL of 50 mM potassium phosphate, pH 6.7, containing 1 mM EDTA, protease inhibitor cocktail, and phosphatase inhibitors. The liver homogenates were then transferred to Eppendorf tubes and centrifuged at 3000 rpm for 10 min at 4 °C. The supernatants were collected as liver lysates for GST activity assay. GST activity was measured using 1-chloro-2,4-dinitrobenzene (CDNB) as the substrate. Briefly, 2 µL of the liver lysates containing 16 µg of protein was diluted at 1:10 with sample buffer and then added to the substrate solution which consisted of 980 µL of PBS, 0.1 mL of 200 mM reduced glutathione, and 100 mM CDNB. The mixtures were then thoroughly mixed and measured for their absorbance at 340 nm measured from 1 to 5 min. One to 5 µL of purified GST was used as the positive control to generate a standard curve for the calculation of GST activities in the samples. The GST activity was expressed as micromole per milliliter-minute [µmol/(mL/min)]. Difference in Gel Electrophoresis (DIGE) Labeling. Cydyes were reconstituted in dimethylformamide (DMF) to a final concentration of 1 mM following the manufacturer’s specifications. In brief, 400 pmol of each of the Cydyes was used to label 50 µg of protein. For the fluorescence labeling of protein samples, each Cydye was mixed with 50 µg of protein sample, respectively, and thoroughly by vortexing, and incubated on ice for 30 min in the dark. One microliter of 10 mM lysine was then added to each mixture to stop the labeling reaction, and the mixture was incubated on ice for 10 min in the dark. All protein samples labeled with different Cydye DIGE fluor minimal dyes (Cy2, Cy3 and Cy5) were combined to form a pool aliquot of all biological samples. The pooled sample Journal of Proteome Research • Vol. 9, No. 3, 2010 1269
research articles mixture was diluted further in the sample buffer (7 M urea, 2 M thiourea, 2% CHAPS (w/v), and 2% IPG) and 2% DTT to the volume of 340 µL for 2-DE analysis. 2-DE Electrophoresis. First-dimension IEF was carried out in an Ettan IPGphor Isoelectric Focusing System as described by the manufacturer. Precast IPG strips (18 cm, pH 3-11 nonlinear) were employed for the first-dimension separation at 20 °C using a three-phase electrophoresis program: 500 V for 1 h, 3000 V for 1 h, and 8000 V for 8 h. Prior to running the second dimension, the proteins in IPG strips were reduced and alkylated by sequential incubation in the equilibration buffer (0.05 M Tris-HCl, pH 8.8, 2% SDS; 30% glycerol, 6 M urea, 0.002% bromophenol blue) containing 10 mg/mL DTT for 15 min; and then the buffer containing 25 mg/mL iodoacetamide. The second-dimension SDS-PAGE was performed overnight at 80 V with temperature control. The resulting 2-DE gels were scanned using Typhoon 9410 scanner for gel images. The voltage used for scanning gels was 550 V. Western Blotting Analysis. Western blot was used to confirm expression changes detected by 2-DE, and to determine GST expression levels in control and mice treated with propiconazole, myclobutanil, and triadimefon. Fifty micrograms of protein was obtained from each of the three mouse livers selected from the treated and control animal groups and subjected to the immunoblot analysis. The SDS-PAGE gels derived underwent electrophoretic transfer to a nitrocellulose membrane. After the protein transfer, the membranes were blocked with 5% nonfat milk for 1 h at room temperature and subsequently incubated overnight with anti-GST mu and alpha antibodies, respectively. The membranes were then washed 4 times with washing buffer (1× PBS, 3% Tween 20) and incubated with horseradish peroxidase (HRP)-conjugated antirabbit secondary antibodies for 1 h. The detection of Western blot signals was performed using chemiluminescence (ECL) kit. Quantification of each protein band was done using the Typhoon scanner and Image Quant software. Beta-actin was used as internal control to monitor variation of protein loadings. 2-DE Gel Image Analysis, Protein Quantitation, and Digestion. For 2-DE analysis, six animals from each control and treated group were used. The scanned 2-DE gel images were imported into Decyder software version 6.5 for protein quantitation analysis. Protein spots showing 1.2-fold change, either increase or decrease, and with a p-value 1.5 for +1 charged ions, Xcorr >2.0 for +2 charged ions, Xcorr >2.5 for +3 charged ions, and a peptide probability score P < 0.5. Protein sequences retrieved from the Bioworks data processing were sorted based on their abundance factors calculated by summing the LC-MS peak heights of their corresponding unique peptides. A search in a composite forward-sequence and a decoy reversedsequence database showed that these filtering settings filtered out all obvious false positive protein entries referring to the decoy database. Pathway and Network Analysis. After protein identification, the accession numbers and fold changes of the differentially expressed proteins were tabulated in Microsoft Excel and imported into IPA (Ingenuity Systems, www.ingenuity.com) for canonical pathway and tox list analyses The statistical significance of each pathways or list was determined by IPA using a Fisher Exact test p < 0.05. IPA was also used to construct networks of interacting proteins. IPA contains a database that uses the most current knowledge available on genes, proteins, chemicals, normal cellular and disease processes, signaling and metabolic pathways, needed for pathway construction. Statistical Analysis for Western Blot and GST Activity Assays. Triplicate gels or activity assays from each experimental group were run. Three animals from each control and treated group were used for Western blot and GST activity assays; a Student’s t test was performed to determine statistical significance between control and conazole-treated samples. Values of protein intensity and activities are presented as mean ( SD. Differences between treatment and control groups were considered statistically significant at p < 0.05.
Results Identification of Differentially Expressed Proteins. Differentially expressed proteins in the livers of mice treated with propiconazole were identified using 2-DE analysis. Six mice from control and propiconazole-treated experimental groups were used and triplicate 2-DE gels were obtained from each sample. From the 3000 matched protein spots, 105 spots were
Proteomic Analysis of Propiconazole Responses
research articles
Figure 1. Detection of differentially expressed proteins by 2-DE in the livers of mice fed propiconazole for 4 days. Representative Cydye-stained 2D gels (control, left panel; propiconazole-treated, right side) performed using livers from propiconazole-treated mice. Sixty-two unique differentially expressed protein spots that were identified by mass spectrometry are marked. The identities of these proteins are listed in Table 1.
found to be significantly (p < 0.01) altered by propiconazole treatment. Forty-six protein spots were up-regulated and 59 spots were down-regulated. Some individual proteins were detected from more than one protein spots, suggesting that different isoforms and/or post-translational modifications of these proteins existed. Sixty-two unique proteins were marked in Figure 1 and identified using MS. Table 1 lists the 62 unique proteins with their identities, functions, pI values, molecular weights p-values, and probability of protein identification such as peptide coverage and XC scores. Each of the 62 proteins passed a false discovery rate analysis filter of p < 0.05. Of these 62 proteins, 30 were down-regulated and 32 were up-regulated. These proteins were classified into the following categories based on their functions (Table 1). They are oxidative stress, proteasome, metabolism of carbohydrate, glucose, lipids, and amino acids, detoxification of xenobiotics, protein binding, folding (chaperones) and transportation, cell growth and differentiation, and antiapoptosis. Validation of Protein Expression Changes Using Western Blotting. Among the identified proteins, GSTs were unique in terms of the numbers of isoforms of the protein detected and the magnitude of fold changes. The GST isoforms (GSTs) identified include GST mu 1, 2, 3, alpha 3, theta 1 and 3 (Table 1). The expression levels of these GSTs were increased 1.73- to 5.46-fold as a result of propiconazole treatment. Western blot analyses were performed to verify the changes detected by the 2-DE DIGE experiments. The liver protein samples obtained from control and propiconazole-treated mice were subjected to one-dimensional SDS-PAGE and blotted with the antibodies against GST mu and GST alpha. Anti-β-actin antibody was used as an internal control (Figure 2). Expression levels of both GST mu and GST alpha were up-regulated (1.6-fold GST mu, 2.9fold GST alpha) by propiconazole treatment in Western blot
studies (Table 2). Thus, Western blot experiments confirmed the results obtained by 2-DE DIGE analysis. Mapping Differentially Expressed Proteins to Canonical Pathways and Tox Lists. IPA software provides the ability to map differentially expressed proteins to fixed canonical pathways and tox lists to uncover those pathways or lists that are altered by toxicant exposure and which may provide clues to toxicity pathways. The 62 differentially expressed proteins were mapped to these pathways and lists, and after removal of duplicate entries (similar pathways and lists), there were 44 pathways and/or lists that were statistically altered (p < 0.05). The general functions of these pathways/lists were the activation of nuclear receptors (e.g., CAR/PXR, PXR/RXR), signaling pathways (e.g., aryl hydrocarbon, hypoxia inducible factor), metabolism of amino acids (e.g., arginine, proline, and cysteine), metabolism of xenobiotics, metabolism of biochemical intermediates (e.g., butanoate, pyruvate, retinol), biosynthesis of biochemical intermediates (e.g., cholesterol, bile acid), and oxidative stress. Five GST isoforms were identified by proteomic analysis and GSTs were present in 10 of the 44 protein-based pathways/lists (data not shown). Comparison of Proteomic and Genomic Pathways/Lists. Our previous genomic study analyzed the livers of mice treated with propiconazole for 4 days using mRNA microarray approaches.8 Using IPA, we mapped the differentially expressed genes to canonical pathways and tox lists and 48 pathway/lists were identified. In the current study, we profiled soluble liver proteins from mice treated with propiconazole under the same conditions as used in the genomic study and used IPA to generate canonical pathways and tox lists. Comparing the two approaches, we found 27 pathways or lists that were similarly altered based on the proteomic and transcriptomic analyses, whereas 17 pathways/lists were only identified by proteomic Journal of Proteome Research • Vol. 9, No. 3, 2010 1271
research articles
Ortiz et al. a
Table 1. Differentially Expressed Soluble Proteins from Livers of Mice Treated for 4 days with Propiconazole protein quantitation gel spot number, protein name and abbreviation 17, 4-hydroxyphenylpyruvic acid dioxygenase (HPD) 54, Aminoacylase 1 (ACY 1) 32, Lactate dehydrogenase A ((LDHA) 41, Carbamoyl phosphate synthetase 1 (CPS 1) 46, Dimethylglycine dehydrogenase precursor (DMGDH) 43, Aminoadipatesemialdehyde synthase precursor (AASS) 52, Hypoxia upregulated 1 (Hyou 1) 58, Succinate coenzyme A ligase, ADP-forming beta subunit (SUCLA 2) 30, Carbonic anhydrase 3 (CA 3) 56, Methionine adenosyltransferase 1 alpha (MAT1A) 48, Formiminotransferase cyclodeaminase (FTCD) 40, Vinculin (VCL) 50, Glutamate dehydrogenase 1 ((GLUD 1) 53, Catechol-omethyltransferase (CATECHOL) 49, Esterase 31 isoform 1 (CES3) 15, Fumarate hydratase 1 (FH 1) 31, Electron transferring flavoprotein, (ETFA) 13, UDP glucose pyrophosphorylase 2 (UGP2) 42, Pyruvate carboxylase (PC) 61, Hydroxyacyl-Coenzyme A dehydrogenase (HADHB) 59, Hypothetical protein LOC70984 (C11ORF54) 21, Acetyl coenzyme A, acetyltransferase 2 (ACAT 2) 25, Proteasome subunit alpha type 7 (PSMA7) 3, Proteasome subunit alpha type 2 (PSMA2) 9, Proteasome 26s subunit non-ATPase subunit 11 (PSMD11) 8, Proteasome 26s subunit ATPase 2 (PSMC 2) 22, Proteasome 26s ATPase subunit 6 (PSMC6) 28, Proteasome 26s, subunit 13 (PSMD 13) 7, A chain A, vcp P97 10, Leucyl aminopeptidase (lap3) 37, UV excision repair protein RAD 23 homologue B (RAD23B) 62, Alpha-2-macroglobulin 35 kDa subunit (PZP) 35, Regucalcin (RGN) 45, Transferrin (TF) 44, Albumin (ALB) 38, BIP-immunoglobulin heavy chain binding protein (HPA5) -also known as HSP70-5 14, Chaperonin subunit 4 delta (CCT4) 39, Tumor rejecting antigen gp96 11, Aldehyde dehydrogenase family 1, subfamily A1 (ALDH1A1)
1272
protein identification
p-value
accession number
matchedpeptides
protein score
1.32
7.20 × 10-3
33859486
56
Amino acid metabolism Amino acid metabolism
-1.31 -1.35
-3
45/6.6
18
260
4.20 × 10 3.00 × 10-3
81880060 6754524
51 60
45.8/5.9 36.5/7.6
15 15
180 236
Amino acid metabolism
-1.46
5.90 × 10-4
Amino acid metabolism
-1.62
4.10 × 10
-4
124248512
52
16.5/6.5
61
1030
21311901
53
97.2/7.8
30
360
Amino acid metabolism
-1.76
9.50 × 10-3
31980703
25
102.9/6.4
18
230
Antiapoptosis
-1.92
2.00 × 10-3
157951706
26
111.1/4.9
19
200
Carbohydrate metabolism
-1.36
8.00 × 10
-3
46849708
39
50.1/6.6
15
252
Carbon dioxide metabolism Cell growth
-1.38
2.90 × 10-3
31982861
37
29.3/7.0
8
90
-1.65
3.00 × 10-4
19526790
44
43.5/5.4
12
140
Cytoskeletal
-1.95
4.30 × 10-4
18252784
16
58.9/5.7
8
90
Cytoskeletal structure Detoxification
-1.22 -1.5
-3
7.70 × 10 8.70 × 10-3
31543942 6680027
47 53
116.6/5.7 61.3/7.9
42 25
704 476
Detoxification
-2.14
1.30 × 10-3
161484634
55
29.5/5.5
10
152
Detoxification
-3.37
2.30 × 10-4
63578904
18
63.3/5.7
8
98
-3
function Amino acid metabolism
fold change
1.90 × 10
sequence coverage (%)
MW (kDa)/pI
Energy metabolism
1.5
33859554
23
54.3/9.4
8
108
Energy metabolism
-1.53
1.30 × 10-4
13097375
61
35.0/8.5
17
212
2.41
3.70 × 10-7
21314832
52
56.9/7.4
22
334
2.00 × 10-3
6679237
49
12.9/6.3
43
610
1.37
2.20 × 10-3
21704100
18.9
51.3/9.8
10
90
3.56
4.20 × 10-8
19526926
54
35.0/5.8
13
266
Lipid metabolism
2.11
6.10 × 10
-3
148747461
22
41.3/7.2
7
70
Proteasome
2.35
4.80 × 10-7
7106389
53
27.8/8.7
11
184
Proteasome
1.8
2.20 × 10-4
1709759
72
25.9/8.6
11
150
-3
134053905
63
47.4/6.1
24
320
Glycogen biosynthesis gluconeogenesis and lipogenesis, Hydratase Hydrolase
-1.4
Proteasome
1.61
2.10 × 10
Proteasome
1.51
6.40 × 10-4
33859604
38
52.8/5.9
16
210
Proteasome
1.48
1.70 × 10-3
34783985
53
43.3/7.3
15
224
1.44
-3
6755210
49
42.8/5.4
17
238
-3
Proteasome
8.50 × 10
Proteasome Proteasome
1.42 1.3
3.10 × 10 7.50 × 10-3
34809664 71059803
63 38
89.2/5 56.1/7.6
42 16
762 298
Proteasome
-1.48
6.90 × 10-3
1709986
30
43.5/4.6
11
120
1.34
3.10 × 10-3
134035924
54
28.2/7.1
13
130
-1.49 -1.83 -1.62 -1.85
-3
4.70 × 10 6.10 × 10-3 1.60 × 10-3 5.90 × 10-4
6677739 20330802 163310765 2598562
66 51 32 49
33.4/5.0 76.7/6.8 68.6/5.7 72.4/4.9
14 33 17 29
236 492 236 370
1.74
8.90 × 10-3
6753322
54
58/8.0
21
276
-2.13
-3
6755863
23
92.4/4.6
14
148
8.80 × 10-4
85861182
55
54.4/7.7
26
458
Protease inhibitor Protein Protein Protein Protein
binding binding binding binding
Protein folding Protein folding Redox
Journal of Proteome Research • Vol. 9, No. 3, 2010
2.21
1.40 × 10
research articles
Proteomic Analysis of Propiconazole Responses Table 1. Continued protein quantitation gel spot number, protein name and abbreviation 19, Carbonyl reductase 1 (CR 1) 18, Aldo-keto reductase family 1 member D1 (AKR1D1) 4, NADPH dehdyrogenase quinone 2 (NQO2) 12, Aldehyde dehydrogenase family 8, member A1 (ALDH8A1) 1, Peroxiredoxin 1 (PRDX1) 16, Cysteine conjugate beta lyase 1 (CCBL1) 34, 3-hydroxyanthranilate 3,4-dioxygenase (HAAO) 57, Aldehyde dehydrogenase 2 (ALDH2) 36, Protein disulfide isomerase precursor (PDI) 60, Thioredoxin domain containing protein 4 precursor (TXNDC4) 20, Aldolase 2 beta isoform (ALDOB) 55, TNF receptorassociated protein 1 (TRAP1) 47, Phospholipase C alpha (PLCR) 23, Glutathione S-Transferase mu 3 (GSTM3) 27, Glutathione S-Transferase theta 3 (GSTT3) 24, Glutathione S-Transferase mu 2 (GSTM2) 29, Glutathione S-Transferase mu 1 (GSTM1) 26, Glutathione S-Transferase alpha 3 (GSTA3) 6, Tyrosine ester sulfotransferase (SULTD1) 2, Glutathione S-Transferasetheta 1 (GSTT1) 5, Indolethylamine N-methyltransferase (INMT) 51, 3-hydroxy-3methylglutaryl coenzyme A synthase 2 (HMGCS2) 33, Sulfotransferase like protein 1 (SULT1D1) a
function Redox
p-value
sequence coverage (%)
MW (kDa)/pI
matchedpeptides
protein score
4.50 × 10-4
113680352
61
30.6/8.4
6
246
-4
21703734
51
37.3/6.5
15
202
fold change 2.14
protein identification accession number
Redox
1.7
1.50 × 10
Redox
1.23
1.20 × 10-3
9937970
46
26.2/6.6
8
100
Redox
1.22
-3
7.00 × 10
30520135
56
53.6/7.4
21
318
Redox Redox
1.2 1.2
4.20 × 10-3 2.60 × 10-3
6754976 31982063
52 29
22.2/8.2 47.5/6.5
10 10
158 164
Redox
-1.53
2.00 × 10-4
17921976
71
32.8/6.1
15
230
-1.69
-3
6753036
37
56.5/7.5
14
160
-3
129729
53
57.1/4.6
24
310
-4
1.10 × 10
19072792
18
46.8/5.0
6
50
1.54
6.80 × 10-4
21450291
34
39.5/8.2
12
160
Signal transduction
-1.43
-3
1.10 × 10
13385998
43
80.2/6.2
23
302
Signal transduction
-2.01
7.00 × 10-6
200397
38
56.6/5.9
18
230
Transferase
5.46
3.80 × 10-7
33468899
57
25.7/7.8
6
358
Transferase
4.47
4.00 × 10-8
21536248
45
27.4/7.6
10
120
Transferase
3.64
2.60 × 10-4
6680121
72
25.9/7.2
18
422
Transferase
3.11
1.20 × 10-6
6754084
51
25.9/8.1
10
408
Transferase
3.06
2.20 × 10-4
31981724
69
25.3/9.1
12
340
Transferase
2.57
9.60 × 10-8
4096440
50
36.7/5.9
13
228
Transferase
1.73
-3
1.00 × 10
2495109
35
27.4/6.9
9
120
Transferase
1.64
2.40 × 10-4
6678281
46
29.4/6.0
11
190
Redox Redox Redox Signal transduction
-1.78 -1.98
6.40 × 10
2.80 × 10
Transferase
-1.5
2.20 × 10-4
31560689
20
56.8/8.4
8
168
Transferase
-1.99
2.40 × 10-3
28202011
60
33.3/6.6
15
208
Proteins were quantified by 2D-DIGE and identified by MS/MS mass spectrometry and protein database matching.
analyses and 21 pathways/lists only identified by genomic analyses (Table 3). Protein Networks. To better understand the interactions between the differentially expressed proteins in response to propiconazole treatment, networks of inter-relationships of these proteins were constructed using IPA software. The 4 highest scored networks generated by IPA were merged to give one overall network (Figure 3). Lines connecting the proteins are genes/proteins which indicate known interrelationships found in the IPA database. Proteins in red are up-regulated and proteins in green are down-regulated. Solid lines represent direct relationships. This combined network displays four major subnetworks, a MYC subnetwork that connects to a second subnetwork of highly interconnected proteasome subunits, a third subnetwork centered around transcription factor hepatocyte nuclear factor 4 alpha (HNF-4R), and the fourth subnetwork of glutathione transferases. Groups of proteins are
interconnected by hubs to form protein clusters of pathways. In addition to HNF-4R, and MYC hubs, there are other hubs that connect significant numbers of proteins: HNF-1R, vinculin (VCL), albumin (ALB), growth factor receptor-bound protein 2 (GRB2), one cut homeobox 1 (onecut1), and heat shock 70 kDa protein 5 (HSPA5). Comparison of Protein Expression Levels and Activities of GST in the Livers of Mice Treated with Conazoles. Since the expression levels of several GSTs were found to be upregulated in propiconazole-treated mice in a consistent and reproducible manner, we studied the GST expression levels and GST activities in the livers of mice treated with other conazoles that were either tumorigenic (triadimefon), and nontumorigenic (myclobutanil). For this purpose, protein expression levels of GST mu and alpha were measured in the livers of mice treated with triadimefon and myclobutanil. Overall, the expression levels measured by Western blot analyses showed a Journal of Proteome Research • Vol. 9, No. 3, 2010 1273
research articles
Figure 2. Western blot analysis of differentially expressed proteins following propiconazole treatment. Liver proteins obtained from mice treated with propiconazole (P), myclobutanil (M) or triadimefon (T) and from controls (C) were subject to Western blot analysis for the detection of GST alpha and mu. The same blot was also reprobed with monoclonal anti-β-actin antibody as a loading control. Three animals from each experimental group were used. The relative protein expression levels and statistical significances were summarized in Table 2.
significant increase of GST alpha and GST mu as a result of triadimefon or propiconazole treatments with respect to the controls. The expression level of GST alpha was not elevated by myclobutanil treatment. GST mu was also not elevated by myclobutanil treatment; however, the expression level of this protein was difficult to ascertain due to a large margin of error of this Western blot experiment (Table 2). Furthermore, we investigated GST activities in the livers of control and conazoletreated mice. Hepatic GST activity was significantly increased in the propiconazole- and triadimefon-treated mice, but not in the myclobutanil-treated mice (Table 2).
Discussion The present study extends and expands the previous genomic studies by profiling the mouse liver proteome and correlating and comparing protein expression profiles with gene expression profiles. A goal of the study was to identify alterations in protein expression and to map these to canonical pathways, tox lists, and networks to identify potential toxicity pathways mechanisms induced by propiconazole treatment, and to identify toxicity biomarkers. There are a number of integrated studies of toxicogenomics and toxicoproteomics focusing on rodent liver toxicity mainly in rats.19,20 Proteomic and genomic profiles of normal mouse liver21 or mouse liver tumors22 have also been reported. This paper is the first report that systematically examines the hepatic effects induced by a conazole using an integrated toxicogenomic and toxicoproteomic approach. Comparison of Proteomic Profiles with Genomic Profiles. Comparisons of the expression results between transcriptomes and proteomes were performed using livers from groups of mice treated with propiconazole under the same treatment conditions in order to identify potential toxicity pathways induced by propiconazole (Table 3). Proteomic profiles of propiconazole-treated mouse livers were obtained using highthroughput 2-DE coupled with nanospray ESI MS/MS, while genomic analyses of microarray mRNA expression profiles provided the transcriptomics data.8 The assumption was that the pathways modified at both protein and mRNA expression levels might be more important than the pathways which were altered at only one level. Canonical pathway and tox list analyses based on proteomic data indicated nuclear receptor activation (e.g., constitutive androstane receptor (CAR)/RXR, 1274
Journal of Proteome Research • Vol. 9, No. 3, 2010
Ortiz et al. PXR, RXR), metabolism of xenobiotics, metabolism of biochemical intermediates (e.g., amino acids, butanoate, inositol, retinol), biosynthesis of biochemical intermediates (e.g., bile acid, cholesterol) and oxidative stress (e.g., NRF2-mediated oxidative stress response, oxidative stress) and several signaling pathways (e.g., aryl hydrocarbon receptor signaling, hypoxiainducible factor signaling, xenobiotic metabolism signaling). Genomic analyses by microarray approaches provided many of the same canonical pathways and tox lists as observed at the proteome level. Both proteomic and transcriptomic analyses gave groups of unique pathways and tox lists not observed by each other. The proteomic analyses identified mainly additional metabolic pathways/lists with the addition of glycolysis/gluconeogenesis, protein ubiquitination, and urea cycle pathways/lists as well as two signaling pathways, aldosterone signaling in epithelial cells and polyamine regulation in colon cancer. The transcriptional analysis gave a series of unique pathways/lists with additional nuclear activation pathways (e.g., liver X receptor (LXR)/RXR, peroxisome proliferator (PPARR)/ RXRR, thyroid receptor (TR)), and a number of additional signaling pathways (e.g., epidermal growth factor signaling (EGF), PI3K/AKT signaling, PTEN signaling). It has been reported that gene expression analysis does not necessarily reflect changes in the corresponding proteins and cellular function and there is some evidence of poor correlation between mRNA and protein abundance.23 This lack of strong correlations could be the result of mRNA degradation, alternative splicing, and post-transcriptional regulation of gene expression. However, in our study, we observe an overlap of 56-61% of pathways/lists from proteomic-genomic results. We suggest that this might be due to the use of similar tissues obtained under the same treatment conditions. Propiconazole Altered Networks. Network analysis of the differentially expressed proteins in response to propiconazole treatment indicated that these proteins were linked through intermediary protein hubs. Many of these proteins have associations with hepatotoxicity and cancer and the alteration of their interconnecting pathways could lead to deleterious effects.24,25 HNFs are transcription factors involved in a wide variety of biological pathways including liver development and function.26 HNF-4R is a liver-enriched member of the steroid hormone receptor superfamily.27 The expression of a variety of genes which are under the control of HNF-4R include apoA1, apoA-II, and apoB genes involved in lipid metabolism pyruvate kinase and dehydrogenase genes in glucose metabolism28,29 and some liver development-related genes.30 As shown in Figure 3, the hub, HNF-4R, was connected to other proteins HPD, FH, SUCLA2, ALDH8A1 AASS, and HAAO, each of which was significantly altered at protein expression levels by propiconazole treatment, suggesting the activation of HNF4R. These proteins are mainly involved in glycolysis and lipid metabolism.31,32 Independent network analysis based on microarray studies of liver mRNA from mice treated with propiconazole under the same conditions also revealed that HNF4R was a hub for a large number of differentially expressed genes.8 It has been reported that HNF-4R is activated mainly through its phosphorylation by MAPK/ERK.33 The phosphorylation modulates HNF-4R DNA binding. MAPK/ERK phosphorylates a number of transcription factors known to alter their transactivation potentials, thereby influencing gene and protein expression to elicit a cellular response.34,35 Both network and pathway/list analyses of the differentially expressed proteins revealed the activation of the nuclear
research articles
Proteomic Analysis of Propiconazole Responses
Table 2. Effects of Propiconazole Treatment on the Expression Levels of GST Alpha and Mu, and GST Activities protein expression levels (fold change mean ( SD)
GST alpha GST mu a
GST activities (fold change mean ( SD)
control
myclobutanil
propiconazole
triadimefon
control
myclobutanil
propiconazole
triadimefon
1.0 ( 0.23 1.0 ( 0.13
1.8 ( 0.41 2.4 ( 1.3
2.9 ( 0.8a 1.6 ( 0.36a
2.9 ( 0.63a 3.1 ( 0.67a
1.0 ( 0.21
0.87 ( 0.19
1.4 ( 0.16a
1.6 ( 0.21a
Statistically significantly different compared to control, p < 0.05.
Table 3. Comparison of Gene and Protein Expression Profiles in the Livers of Mice Treated with Propiconazolea
pathway or list Pathway Pathway Pathway Pathway and List Pathway Pathway Pathway List List Pathway Pathway Pathway Pathway and List Pathway Pathway Pathway Pathway Pathway List Pathway Pathway and List Pathway Pathway Pathway Pathway List Pathway Pathway and List Pathway List Pathway Pathway Pathway Pathway Pathway and List Pathway Pathway Pathway Pathway Pathway Pathway Pathway List Pathway
b
ingenuity canonical pathways and tox lists β-alanine Metabolism Aldosterone Signaling in Epithelial Cells Arginine and Proline Metabolism Aryl Hydrocarbon Receptor Signaling Ascorbate and Aldarate Metabolism Bile Acid Biosynthesis Butanoate Metabolism CAR/RXR Activation Cholesterol Biosynthesis Citrate Cycle Cysteine Metabolism D-glutamine and D-glutamate Metabolism Fatty Acid Metabolism Glutamate Metabolism Glutathione Metabolism Glycolysis/Gluconeogenesis Histidine Metabolism Hypoxia Signaling in the Cardiovascular System Hypoxia-Inducible Factor Signaling Inositol Metabolism LPS/IL-1 Mediated Inhibition of RXR Function Lysine Biosynthesis Lysine Degradation Metabolism of Xenobiotics by Cytochrome P450 Methionine Metabolism Negative Acute phase Response Proteins Nitrogen Metabolism NRF2-mediated Oxidative Stress Response One Carbon Pool by Folate Oxidative Stress Phenylalanine Metabolism Polyamine Regulation of Colon Cancer Propanoate Metabolism Protein Ubiquitination Pathway PXR/RXR Activation Pyruvate Metabolism Retinol Metabolism Synthesis and Degradation of Ketone Bodies Tryptophan Metabolism Tyrosine Metabolism Urea Cycle and Metabolism of Amino Groups Valine, Leucine and Isoleucine Degradation Xenobiotic Metabolism Xenobiotic Metabolism Signaling
also present in transcript analysisb
X X X X X X X X X X X
X X X X
X X X
X X X X X X
X X X
a Each pathway or list was statistically significant (p < 0.05). Pathways and lists based on genomic analyses from Nesnow et al.8
receptor PXR through the heterodimer PXR/RXR. PXR/RXR activation controls the transcription of many genes including CYP3A4, a phase I enzyme that is responsible for the metabolism of many drugs and phase II conjugating enzymes such as
GSTs. In previous studies in mice, propiconazole treatment induced Cyp3a11 and Cyp3a13 mRNA as well as CYP3A protein.5 Several proteins interconnected to the PXR/RXR system such as CES3 and ALDH1A1 were also altered at the protein expression level. In addition, the retinol metabolism canonical pathway was also altered (Table 3). Retinoic acid plays a pivotal role in cellular differentiation and cell proliferation in various tissues including liver.36 Taken together, these results, alterations in glycolytic pathways, gluconeogenesis and/ or lipogenesis pathways, may be altered by propiconazole, and altered glucose utilization and/or lipid metabolism suggest potential adverse hepatic processes that might lead to liver cancer.37 Another class of proteins that were altered significantly by propiconazole treatment were the proteosome proteins including PSMA, PSMA2, PSMA 6, PSMA7, P700/20S, PSMC2, PSMC, PSMD11 and PSMD13. All these proteins were up-regulated and interconnected to each other. Most of these proteins had not previously been linked to propiconazole-induced effects. The ubiquitin-proteasome pathways are the major proteolytic systems in the cytosol of eukaryotic cells, catalyzing the selective degradation of short-lived proteins and the rapid elimination of proteins with abnormal conformation.38,39 It has been shown to be involved in various biological processes such as cell cycle, apoptosis and inflammation which are contributors to liver toxicity and carcinogenicity.38-40 While the effects of the decreased expression of proteosome proteins by propiconazole on overall protein degradation or on specific proteins are not known, it is clear that the activation of proteosome proteins and pathways may be important events in propiconazole-induced liver toxicity. MYC was found to be a major hub in the merged network interacting with many proteins. Myc is a protooncogene and when activated results in many diverse biological effects. Myc can control cell proliferation by up-regulating cyclins, and down-regulating p21,41 and can control apoptosis by downregulating Bcl-2.42 Myc is often found to be up-regulated in human cancers.43 Liver injury and disease including liver cancer are usually characterized by chronic inflammation of the organ and are associated with an increased production of ROS.44 The correlation between liver toxicity, damage or disease and increased ROS has been reported.45,46 Almost all types of liver injury are associated with an increased production of ROS. ROS-generated oxidative stress is a major toxic event occurring in several liver diseases.45,46 ROS affects major cellular components including DNA, RNA, lipids, and proteins,47-49 and are involved in the regulation of hepatocyte apoptosis and cell proliferation. ROS can also function as the second messengers to activate protein kinases or phosphatases to activate/deactivate transcription factors such as NF-KB, Nrf-2, and thereby control gene and protein expression profile.50-52 Moreover, ROS could potentiate their own effects by influencing transcription and activation of cytokines and growth factors which are responsible for Journal of Proteome Research • Vol. 9, No. 3, 2010 1275
research articles
Ortiz et al.
Figure 3. Biological network of differentially expressed proteins showing inter-relationships and relevant signaling pathways. The network was generated by IPA and was obtained by merging the 4 highest scored networks into one overall network. Four subnetworks are highlighted with thick circles. Thin circles identify proteins that also serve as hubs for groups of proteins. Proteins in red are the up-regulated, while proteins in green are down-regulated. Solid lines represent direct relationships. Lines connecting the proteins indicate known interrelationships from the IPA database.
further ROS production.53 In the previous genomic investigations, SOD, catalase, and H2O2 metabolism-related enzymes or pathways remained unchanged, suggesting H2O2 might not be a major contributor of ROS in the livers of mice treated with propiconazole. However, several proteins involved in H2O2 mechanisms such as MATTA, GLUD1, and ALDH2 were all down-regulated by propiconazole. This suggests that the concentration of H2O2 and/or other ROS was increased by propiconazole. H2O2 generation in peroxisome proliferatorinduced oxidative stress and oncogenesis has been previously reported.54 The resulting unbalance of the H2O2 could ultimately impair important enzymatic functions and induce liver toxicity. The potential increase in H2O2/ROS production could be an alternative mechanism through which oxidative stress is increased by propiconazole. Expression and Activity of GST in the Livers of Mice Treated with Conazoles. GST catalyzes the nucleophilic attack by reduced glutathione on nonpolar compounds that contain an electrophilic carbon, nitrogen, or sulfur atom, resulting in the formation of less reactive, more hydrophilic glutathione conjugates.55,56 It achieves detoxification by catalyzing the conjugation of reduced glutathione to various electrophilic substrates. Subtypes of GSTs have been grouped on the basis of isoelectric point, substrate and inhibitor properties, antibody recognition and other factors.57 There are eight classes of 1276
Journal of Proteome Research • Vol. 9, No. 3, 2010
mammalian soluble GST (alpha, mu, pi, theta, zeta, sigma, pi and kappa).57,58 GSTs represent the largest family of proteins, and have been suggested as liver toxicity biomarkers and important determinants of the susceptibility of the tissue to physiological or pathological changes.59,60 The expression patterns of GSTs as biomarkers for carcinogenesis and chemical toxicity of phenobarbital and 3-methylcholanthrene exposure has been previously reported.61 In the present study, the significant increases in both protein expression levels and activities of GSTs in the livers of mice treated with the tumorigenic propiconazole and triadimefon, but not the nontumorigenic myclobutanil, make GST a potential toxicity biomarker in response to tumorigenic conazole treatment. The differential responses of GST activities between the 3 conazoles are intriguing and deserve additional studies to examine the potential of GST as a predictive toxicity biomarker of tumorigenic conazoles. Conclusion. In summary, we have described the proteomic profile of livers of mice treated with propiconazole using 2-DE coupled with nanospray LC-MS/MS. Many liver proteins and pathways that were modulated in the early response to propiconazole treatment have been identified in the present study. These proteins were involved in nuclear receptor activation, metabolism of xenobiotics, metabolism of biochemical intermediates, biosynthesis of biochemical intermediates, transcrip-
research articles
Proteomic Analysis of Propiconazole Responses tion/translation processes, inflammation, glucose and lipid metabolism, alteration of the signaling networks, stress cellular response, and protein degradation pathways and oxidative stress. Pathway/list and network analyses provided an integrated picture of the effects of propiconazole in mouse liver. By comparing the proteomic profiles with the genomic profiles, the present study provides information useful for the understanding of roles of these alterations in regard to cellular toxicity pathways and toxicity mechanisms of propiconazole. Finally, the differential expression and activities of GSTs in response to tumorigenic and nontumorigenic conazoles was also investigated. In conclusion, this study represents the first application of both proteomic and genomic analysis of the effects of propiconazole in mouse liver and provides novel insight into the potential toxicity pathways induced by this agent.
(12) (13) (14) (15) (16)
(17)
Acknowledgment. The authors would like to thank Barbara Collins, Drs. James Allen, Jeffrey Ross, Doug Wolf, and Ram Ramabhadran for their very helpful comments on this manuscript, and Dr. Lyle Burgoon and Dr. Susan Hester for performing the false discovery rate calculations. The information in this document has been funded wholly by the U.S. Environmental Protection Agency. It has been subjected to review by the National Health and Environmental Effects Research Laboratory and approved for publication. Approval does not signify that the contents reflect the views of the Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.
(18)
(19)
(20)
(21)
References (1) Zarn, J. A.; Bruschweller, B. J.; Schlatter, J. R. Azole fungicides affect mammalian steroidogenesis by inhibiting sterol 14 alphademethylase and aromatase. Environ. Health Perspect. 2003, 111, 255–261. (2) INCHEM. Monograph 768. Propiconazole Part II Toxicology. In Pesticide Residues in Food: 1987 Evaluations, 1987; p 19. (3) U.S. Environmental Protection Agency. Reregistration Eligibility Decision (RED) for Propiconazole. http://www.epa.gov/oppsrrd1/ REDs/propiconazole_red.pdf. (4) Allen, J. W.; Wolf, D. C.; George, M. H.; Hester, S. D.; Sun, G.; Thai, S. F.; Delker, D. A.; Moore, T.; Jones, C.; Nelson, G.; Roop, B. C.; Leavitt, S.; Winkfield, E.; Ward, W. O.; Nesnow, S. Toxicity profiles in mice treated with hepatotumorigenic and non-hepatotumorigenic triazole conazole fungicides. Propiconazole, triadimefon and myclobutanil. Toxicol. Pathol. 2006, 34, 853–862. (5) Chen, P. J.; Padgett, W. T.; Moore, T.; Winnik, W.; Lambert, G. R.; Thai, S. F.; Hester, S. D.; Nesnow, S. Three conazoles increase hepatic microsomal retinoic acid metabolism and decrease mouse hepatic retinoic acid levels in vivo. Toxicol. Appl. Pharmacol. 2009, 234, 143–155. (6) Sun, G.; Thai, S. F.; Tully, D. B.; Lambert, G. R.; Goetz, A. K.; Wolf, D. C.; Dix, D. J.; Nesnow, S. Propiconazole-induced cytochrome P450 gene expression and enzymatic activities in rat and mouse liver. Toxicol. Lett. 2005, 155, 277–287. (7) Ward, W. O.; Delker, D. A.; Hester, S. D.; Thai, S. F.; Wolf, D. C.; Allen, J. W.; Nesnow, S. Transcriptional profiles in liver from mice treated with hepatotumorigenic and non-hepatotumorigenic triazole conazole fungicides: Propiconazole, triadimefon, and myclobutanil. Toxicol. Pathol. 2006, 34, 863–878. (8) Nesnow, S.; Ward, W.; Moore, T.; Ren, H.; Hester, S. D. Discrimination of tumorigenic triazole conazoles from phenobarbital by transcriptional analyses of mouse liver gene expression. Toxicol. Sci. 2009, 110, 68–83. (9) Bruno, M.; Moore, T.; Nesnow, S.; Ge, Y. Protein carbonyl formation in response to propiconazole-induced oxidative stress. J. Proteome Res. 2009, 8, 2070–2078. (10) Ross, J. A.; Moore, T.; Leavitt, S. A. In vivo mutagenicity of conazole fungicides correlates with tumorigenicity. Mutagenesis 2008, 24, 149–152. (11) Carmichael, N. G.; Enzmann, H.; Pate, I.; Waechter, F. The significance of mouse liver tumor formation for carcinogenic risk
(22)
(23) (24)
(25) (26) (27) (28)
(29)
(30) (31) (32)
(33)
assessment: results and conclusions from a survey of ten years of testing by the agrochemical industry. Environ. Health Perspect. 1997, 105, 1196–1203. Kawahara, H.; Cadrin, M.; French, S. W. Ethanol-induced phosphorylation of cytokeratin in cultured hepatocytes. Life Sci. 1990, 47, 859–863. Merrick, B. A.; Bruno, M. E. Genomic and proteomic profiling for biomarkers and signature profiles of toxicity. Curr. Opin. Mol. Ther. 2004, 6, 600–607. Pan, C.; Gnad, F.; Olsen, J. V.; Mann, M. Quantitative phosphoproteome analysis of a mouse liver cell line reveals specificity of phosphatase inhibitors. Proteomics 2008, 8, 4534–4546. Sun, X.; Li, B.; Li, X.; Wang, Y.; Sun, G. F. Effects of sodium arsenite on catalase activity, gene and protein expression in HaCaT cells. Toxicol. in Vitro 2006, 20, 1139–144. Kang, D. H.; Gho, Y. S.; Suh, M.; Chulhun Kang, C. Highly Sensitive and Fast Protein Detection with Coomassie Brilliant Blue in Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis. Bull. Korean Chem. Soc. 2002, 23, 1511–1512. Ge, Y.; Molloy, M. P.; Chamberlain, J. S.; Andrews, P. C. Differential expression of the skeletal muscle proteome in mdx mice at different ages. Electrophoresis 2004, 25, 2576–2585. Rosenfeld, J.; Capdevielle, J.; Guillermot, J. C.; Ferrara, P. In-gel digestion of proteins for internal sequence analysis after one or two-dimensional gel electrophoresis. Anal. Biochem. 1992, 203, 173–179. Klenø, T. G.; Kiehr, B.; Baunsgaard, D.; Sidelmann, U. G. Combination of ‘omics’ data to investigate the mechanism(s) of hydrazineinduced hepatotoxicity in rats and to identify potential biomarkers. Biomarkers 2004, 9, 161–138. Craig, A.; Sidaway, J.; Holmes, E.; Orton, T.; Jackson, D.; Rowlinson, R.; Nickson, J.; Tonge, R.; Wilson, I.; Nicholson, J. Systems toxicology: integrated genomic, proteomic and metabonomic analysis of methapyrilene induced hepatotoxicity in the rat. J Proteome Res. 2006, 7, 1586–1601. Mijalski, T.; Harder, A.; Halder, T.; Kersten, M.; Horsch, M.; Strom, T. M.; Liebscher, H. V.; Lottspeich, F.; de Angelis, M. H. Identification of coexpressed gene clusters in a comparative analysis of transcriptome and proteome in mouse tissues. Proc. Natl. Acad. Sci. U.S.A. 2005, 102, 8621–8626. Rignall, B.; Ittrich, C.; Krause, E.; Appel, K. E.; Buchmann, A.; Schwarz, M. Comparative transcriptome and proteome analysis of Ha-ras and B-raf mutated mouse liver tumors. J. Proteome Res. 2009, 8 (8), 3987–3994. Nie, L.; Wu, G.; Culley, D. E.; Scholten, J. C.; Zhang, W. Integrative analysis of transcriptomic and proteomic data: challenges, solutions and applications. Crit. Rev. Biotechnol. 2007, 27, 63–75. Cobbe, S. C.; Scobie, G. C.; Pohler, E.; Hayes, J. D.; Kernohan, N. M.; Dillon, J. F. Alteration of glutathione S-transferase levels in Barrett’s metaplasia compared to normal oesophageal epithelium. Eur. J. Gastroenterol. Hepatol. 2003, 15, 41–47. Sheweita, S. A.; Tilmisany, A. K. Cancer and phase II drugmetabolizing enzymes. Curr. Drug Metab. 2003, 4, 45–58. Nagaki, M.; Moriwaki, H. Transcription factor and hepatocyte differentiation. Hepatol. Res. 2008, 38, 961–969. Zhong, W.; Mirkovitch, J.; Darnell, J. E., Jr. Tissue-specific regulation of mouse hepatocyte nuclear factor 4 expression. Mol. Cell. Biol. 1994, 14, 7276–7284. Wang, H.; Maeschler, P.; Antinozzi, P. A.; Hagenfldt, K. A.; Wollheim, C. B. Hepatocyte nuclear factor 4 alpha regulates the expression of pancreatic beta-cell genes implicated in glucose metabolism and nutrient-induced insulin secretion. J. Biol. Chem. 2000, 275, 35953–35959. Leclerc, I.; Lenzner, C.; Gourdon, L.; Vaulont, S.; Kahn, A.; Viollet, B. Hepatocyte nuclear factor-4 alpha involved in type 1 maturityonset diabetes of the young is a novel target of AMP-activated protein kinase. Diabetes 2001, 50, 1515–1521. Boj, S. F.; Parrrizas, M.; Maestro, M. A.; Ferrer, J. A transcription factor regulatory circuit in differentiated pancreatic cells. Proc. Natl. Acad. Sci. U.S.A. 2001, 98, 14481–14486. Li, K.; Zhang, H.; Wang, Y.; Wang, Y.; Feng, M. Differential expression of HNF4 alpha isoforms in liver stem cells and hepatocytes. J. Cell Biochem. 2006, 99, 558–564. Parviz, F.; Matullo, C.; Garrison, W. D.; Savatski, L.; Adamson, J. W.; Ning, G.; Kaestner, K. H.; Rossi, J. M.; Zaret, K. S.; Dunc, S. A. Hepatocyte nuclear factor 4 alpha controls the development of a hepatic epithelium and liver morphogenesis. Nat. Genet. 2003, 34, 292–296. Xu, Z.; Tavares-Sanchez, O. L.; Li, Q.; Fernando, J.; Rodriguez, C. M.; Studer, E. J.; Pandak, W. M.; Hylemon, P. B.; Gil, G. Activation of bile acid biosynthesis by the p38 mitogen-activated protein kinase
Journal of Proteome Research • Vol. 9, No. 3, 2010 1277
research articles
(34)
(35)
(36)
(37)
(38)
(39) (40)
(41)
(42) (43) (44)
(45)
(46)
(47)
1278
(MAPK): hepatocyte nuclear factor-4alpha phosphorylation by the p38 MAPK is required for cholesterol 7alpha-hydroxylase expression. J. Biol. Chem. 2007, 284, 24607–24614. Tanos, T.; Marinissen, M. J.; Leskow, F. C.; Hochbaum, D.; Martinetto, H.; Gutkind, J. S.; Coso, O. A. Phosphorylation of c-Fos by members of the p38 MAPK family. Role in the AP-1 response to UV light. J. Biol. Chem. 2005, 280, 18842–18852. Lopez-Bergami, P.; Huang, C.; Goydos, J. S.; Yip, D.; Bar-Eli, M.; Herlyn, M.; Smalley, K. S.; Mahale, A.; Eroshkin, A.; Aaronson, S.; Ronai, Z. Rewired ERK-JNK signaling pathways in melanoma. Cancer Cell 2007, 11, 447–460. Ogura, Y.; Suruga, K.; Takase, S.; Goda, T. Developmental changes of the expression of the genes regulated by retinoic acid in the small intestine of rats. Life Sci. 2005, 77, 2804–2813. Abdelmegeed, M. A.; Moon, K. H.; Hardwick, J. P.; Gonzalez, F. J.; Song, B. J. Role of peroxisome proliferator-activated receptor-alpha in fasting-mediated oxidative stress. Free Radical Biol. Med. 2009, 47, 767–778. Wang, J.; Maldonado, M. A. The ubiquitin-proteasome system and its role in inflammatory and autoimmune diseases. Cell Mol. Immunol. 2006, 4, 55–61. Reinstein, E. Immunologic aspects of protein degradation by the ubiquitin-proteasome system. Isr. Med. Assoc. J. 2004, 6, 420–424. Bochtler, M.; Ditzel, L.; Groll, M.; Hartmann, C.; Huber, R. The proteasome. Annu. Rev. Biophys. Biomol. Struct. 1999, 28, 295– 317. Chen, H.; Liu, J.; Zhao, C. Q.; Diwan, B. A.; Merrick, B. A.; Waalkes, M. P. Association of c-myc overexpression and hyperproliferation with arsenite-induced malignant transformation. Toxicol. Appl. Pharmacol. 2001, 175, 260–268. Nilsson, J. A.; Cleveland, J. L. Myc pathways provoking cell suicide and cancer. Oncogene 2003, 22, 9007–9021. Pelengaris, S.; Khan, M.; Evan, G. c-MYC: more than just a matter of life and death. Nat. Rev. Cancer 2002, 2, 764–776. Macarthur, M.; Georgina, L.; Hold, G. L.; El-Omar, E. M. Inflammation and Cancer II. Role of chronic inflammation and cytokine gene polymorphisms in the pathogenesis of gastrointestinal malignancy. Am. J. Physiol.: Gastrointest. Liver Physiol. 2004, 286, G515–G520. Zegura, B.; Lah, T. T.; Filipic, M. Alteration of intracellular GSH levels and its role in microcystin-LR-induced DNA damage in human hepatoma HepG2 cells. Mutat. Res. 2006, 611, 25–33. Cohen, J. I.; Roychowdhury, S.; DiBello, P. M.; Jacobsen, D. W.; Nagy, L. E. Exogenous thioredoxin prevents ethanol-induced oxidative damage and apoptosis in mouse liver. Hepatology 2009, 49, 1709–1717. Liu, P. K.; Robertson, C. S.; Valadka, A. The association between neuronal nitric oxide synthase and neuronal sensitivity in the brain after brain injury. Ann. N.Y. Acad. Sci. 2002, 962, 226–241.
Journal of Proteome Research • Vol. 9, No. 3, 2010
Ortiz et al. (48) Wang, F.; Gao, F.; Lan, M.; Yuan, H.; Huang, Y.; Liu, J. Oxidative stress contributes to silica nanoparticle-induced cytotoxicity in human embryonic kidney cells. Toxicol. in Vitro 2009, 23, 808– 815. (49) Cederbaum, A. I.; Lu, Y.; Wu, D. Role of oxidative stress in alcoholinduced liver injury. Arch. Toxicol. 2009, 83, 519–548. (50) Barchowsky, A.; Munro, S. R.; Morana, S. J.; Vincenti, M. P.; Treadwell, M. Oxidant-sensitive and phosphorylation -dependent activation of NFkappa B and AP-1 in endothelial cells. Am. J. Physiol. 1995, 269, 829–836. (51) Rushmore, T. H.; Kong, A. N. Pharmacogenomics, regulation and signaling pathways of phase 1 and II drug metabolizing enzymes. Curr. Drug Metab. 2002, 3, 481–490. (52) Chen, L.; Liu, L.; Huang, S. Cadmium activates the mitogenactivated protein kinase (MAPK) pathway via induction of reactive oxygen species and inhibiton of protein phosphatases 2A and 5. Free Radical Biol. Med. 2008, 45, 1035–1044. (53) Yamamoto, S.; Shimizu, S.; Kiyonaka, S.; Takahashi, N.; Wajima, T.; Hara, Y.; Negoro, T.; Hiroi, T.; Kiuchi, Y.; Okada, T.; Kaneko, S.; Lange, I.; Fleig, A.; Penner, R.; Nishi, M.; Takeshima, H.; Mori, Y. Nat Med. TRPM2-mediated Ca2+influx induces chemokine production in monocytes that aggravates inflammatory neutrophil infiltration. Nat. Med. 2008, 14, 738–747. (54) Yeldandi, A. V.; Rao, M. S.; Reddy, J. K. Hydrogen peroxide generation in peroxisome proliferator-induced oncogenesis. Mutat. Res. 2000, 448, 159–177. (55) Knight, T. R.; Supratim Choudhuri, S.; Klaassen, C. D. Induction of Hepatic Glutathione S-Transferases in Male Mice by Prototypes of Various Classes of Microsomal Enzyme Inducers. Toxicol. Sci. 2008, 106, 329–338. (56) Hayes, H. D.; Flanagan, J. U.; Jowsey, I. R. Glutathione transferases. Annu. Rev. Pharmacol. Toxicol. 2005, 45, 51–88. (57) Mannervik, B.; Board, P. G.; Hayes, J. D.; Listowski, I.; Pearson, W. R. Nomenclature for mammalian soluble glutathione trasnferases. Methods Enzymol. 2005, 401, 1–8. (58) Thomson, R. E.; Bigley, A. L.; Foster, J. R.; Jowsey, I. R.; Elcombe, C. R.; Orton, T. C.; Hayes, J. D. Tissue-specific expression and subcellular distribution of murine glutathione S-transferase class kappa. J. Histochem. Cytochem. 2009, 52, 653–662. (59) Shaw, M. The use of histological defined specific biomarkers in drug development with special reference to the glutathione s-transferases. Cancer Biomarkers 2005, 1, 69–74. (60) Vos, R. M.; Bladeren, P. J. Glutathione S-Transferases in relation to their role in the biotransformation of xenobiotics. Chem.-Biol. Interact. 1990, 75, 241–265. (61) Thyagaraju, K.; Hemavathi, B.; Vasundhara, K.; Rao, A. D.; Devi, K. N. Comparative study on glutathione transferases on rat brain and testis under the stress of phenobarbitol and beta-methylcholanthrene. J. Zhejian Univ., Sci., B 2005, 6, 759–769.
PR900755Q
