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Relative quantification of the proteomic changes associated with the mycotoxin zearalenone in the H295R steroidogenesis model
Øyvind L. Buska, Doreen Ndossi b, Steven Verhaegen b, Lisa Connolly“, Gunnar Eriksen d, Erik Ropstad b, Morten Sørlie a,*
a Department of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
b Norwegian School of Veterinary Science, Oslo, Norway
” The Institute of Agri-food and Land Use, School of Biological Sciences, Queen’s University Belfast, Ireland
d National Veterinary Institute, Oslo, Norway
ARTICLE INFO
Article history: Received 27 June 2011 Received in revised form 23 August 2011 Accepted 25 August 2011
Available online 1 September 2011
Keywords: Zearalenone Mycotoxins Quantitative proteomics
ABSTRACT
Zearalenone (ZEN) is a mycotoxin with endocrine disrupting effects having vast economic implications in e.g. pig farming. Structurally, ZEN resembles 17ß-estradiol, and thus is able to bind to estrogen receptors (ER) in target cells. Because of this, it is also classified as a non-steroidal estrogen, a phytoestrogen, a mycoestrogen, and a growth promoter. Quantitative proteomic analysis was undertaken using stable-isotope labeling by amino acids in cell culture (SILAC) upon exposure of the steroidogenesis cell model H295R with ZEN to elucidate its effect on protein regulation. ZEN significantly regulated 21 proteins, including proteins with known endocrine disrupting effects and several oncogenes. In addition, network analysis using Ingenuity Pathway Analysis showed that ZEN affected the oxidative phosphorylation pathway and the mitochondrial dysfunction pathway, both previously reported to be involved in endocrine dysfunction.
@ 2011 Elsevier Ltd. All rights reserved.
1. Introduction
Zearalenone (ZEN) (Fig. 1) is a mycotoxin produced by Fusarium graminearum, Fusarium culmorum, Fusarium equiseti, and Fusarium crookwellense worldwide in a number of cereal crops such as maize barley, oats, wheat, rice and sorghum (Bennett and Klich, 2003). Biotransfor- mation of ZEN forms the metabolites a .- zearalenol (a-ZOL) and ß-zearalenol (ß-ZOL) (Fig. 1) and these metabolites can be further reduced to form a-zearalanol (a .- ZAL) and ß- zearalanol (ß-ZAL). In addition, both ZEN and all the reduced metabolites are glucuronide conjugated (Kleinova et al., 2002; Kuiper-Goodman et al., 1987). There are species differences on ZEN biotransformation where the dominant hepatic metabolite is ß-ZOL in cattle and chickens
(Malekinejad et al., 2006) and a .- ZOL in pigs and humans (Pillay et al., 2002; Videmann et al., 2008).
Structurally, ZEN resembles 17ß-estradiol (Fig. 2), and thus is able to bind to estrogen receptors (ER) in target cells. Because of this, it is also classified as a non-steroidal estrogen, a phytoestrogen, and as a mycoestrogen (Bennett and Klich, 2003). It is a stable compound not easily degraded by common food and feed processing procedures (JECFA, 2000). The estrogenic activity of ZEN and its implication in reproductive performance in swine and other domestic animals both in vivo and in vitro have long been reported (Chang et al., 1979; Etienne and Jemmali, 1982). Following exposure, ZEN has been shown to decrease fertility, cause reproductive tract disorders, result in abnormal fetal development, reduce litter size, and lead to changes in the weight of adrenal and pituitary glands in animals (Creppy, 2002; Tiemann and Danicke, 2007). Recently, ZEN has been associated with development of central precocious puberty (CPP) in girls in North-West
* Corresponding author. Tel .: +47 64965902; fax: +47 64965901. E-mail address: morten.sorlie@umb.no (M. Sørlie).
OH
O
O
HO
0
zearalenone (ZEN)
OH
0
OH
O
O
O
HO
OH
HO
OH
a-zearalenol (a-ZOL)
ß-zearalenol (ß-ZOL)
OH
0
OH
o
O
O
HO
OH
HO
OH
a-zearalanol (a-ZAL)
ß-zearalanol (ß-ZAL)
region of Tuscany where increased serum levels of ZEN and a-ZOL were linked to the condition in six of the 17 affected girls (Massart et al., 2008).
In in vitro studies, ZEN is reported to bind to the estrogen receptors (ER) in target tissues and cells with a binding affinity of between 1% to 10% of the binding affinity of 17ß-estradiol whereas a-ZOL showed stronger binding and ß-ZOL much weaker (Kuiper-Goodman et al., 1987). ZEN is also reported to stimulate the transcrip- tional activity of both estrogen receptor (ER) a, and ERß at a concentration of 1-10 nM/L, and was found to be a full agonist of ERo. and a mixed agonist-antagonist for ERB (Kuiper et al., 1998). ZEN and a-ZOL have been demon- strated as potent inhibitors of testosterone production in mouse Leydig cells where ZEN impaired the abundance of
OH
H
H
=
HO
17ß-estradiol
several essential rate limiting enzymes: 3ß-hydroxysteroid dehydrogenase-1 (3-B-HSD-1); Cytochrome P450, family 11, subfamily A, polypeptide 1 (P450scc); Steroidogenic acute regulatory protein (StAR) (Yang et al., 2007b).
Endocrine disruption may involve the potential disruption of hormone signaling and metabolism, receptor protein degradation, sensitization by short-chain fatty acid exposure, altered DNA-methylation and effects on recep- tors (Tabb and Blumberg, 2006). To date, the effect of ZEN has been studied in different cell models with respect to hormone production (Yang et al., 2007a), receptor binding and affinity (Fitzpatrick et al., 1989), gene expression (Mayr, 1988), and nuclear receptor activation (Ding et al., 2006). H295R cells derived from human adrenal carcinoma have similar physiological characteristics of zonally undifferen- tiated human fetal adrenal cells, and thus have the impor- tant enzymes for steroidogenesis as of the adult adrenal cortex (Gazdar et al., 1990; Staels et al., 1993). H295R is a cell line that integrates the effects of direct acting hormone agonists and antagonists, as well as chemicals affecting signal transduction pathways for steroid hormone production. It is therefore a useful tool for examining effects of chemicals on steroidogenesis by studying both gene expression and hormone secretion (Gracia et al., 2006; Hilscherova et al., 2004).
In this work, we have exposed H295R cells to zear- alenone to study its endocrine effect as observed through up- and down-regulation of proteins determined by quantitative and qualitative proteomic using Stable Isotope Labeling of Amino Acids in Cell Culture (SILAC) and gel electrophoresis-liquid chromatography-mass spectrom- etry/mass spectrometry (1D GEL-LC-MS/MS) (Ong et al., 2002).
2. Materials and methods
2.1. Chemicals and media
Routine cell culture media were obtained from Invitrogen (Karlsruhe, Germany). Cell culture medium lacking L-arginine and L-lysine and amino acids were purchased from Pierce (Rockford, IL, USA). ITS+ Premix containing human trans- ferrin, linoleic acid, selenous acid to stimulate cell prolifera- tion and NuSerum were purchased from BD Biosciences. Nu- PAGE gels were purchased from Invitrogen. Subcellular pro- teome extraction kit was purchased from Calbiochem (Darmstadt, Germany). Trypsin for digestion of proteins was from Promega (Madison, WI, USA). DTT and iodoacetamide were purchased from Sigma-Aldrich (St. Louis, MO, USA). Deionised filtered water (Milli Q, Millipore Inc., Jeffrey, NH) was used in all experiments. All other reagents were of analytical grade and purchased at standard manufacturer.
2.2. Cell culture
H295R cells (ATCC number: CRL-2128 NCI-H295R) were grown in a 1:1 mixture of Dulbecco’s Modified Eagle’s Medium (DMEM) and Ham’s F-12 Nutrient mixture con- taining 15 mM HEPES. Supplements consisted of ITS+ Premix (5 ml/500 ml) and NuSerum (12.5 ml/500 ml medium). No antibiotics or fungicides were used. Cells were grown in 75 cm3 flasks in a humidified incubator at 37 ℃ (95% air, 5% CO2) and passaged once weekly and changed to SILAC-medium between passage 4 and 13 one week prior to the exposure. H295R cells were split into a “light” and “heavy” population that were cultured sepa- rately. This culture medium was L-arginine and L-lysine free. Arginine and lysine were added to the medium in two different forms. The “light” medium was supplemented with normal (light) amino acids (12C6-arginine and lysine), while the “heavy” medium was supplemented with heavy (13C6-arginine and lysine) amino acids. This ensured complete incorporation of the heavy and light Arg and Lys The day before exposure cells were passaged with the density adjusted 150,000 cells/cm2. The cells were allowed to reattach for 24 h. On the day of exposure, medium was changed and the “light” population used as a solvent control (DMSO 0.1%). The “heavy” population was exposed to ZEN (10 µM) dissolved in DMSO (0.1% final). Exposed cells were cultured for 48 h. The cells were incubated with 10 µM ZEN, which does not cause cytotoxicity in the H295R cell line (Frizzell et al., 2011). Three biological replicates were performed.
2.3. Fractionation and SDS-PAGE
Prior protein extraction, medium was discarded and the cells lysed on a rocking platform at 4 ℃ in the various buffers provided with the Subcellular proteome extraction kit (Calbiochem) according to the manufacturer’s instruc- tions. After fractionation, the samples containing the light and heavy amino acids were combined in a 1:1 ratio (SDS- PAGE was used to obtain visually as close to 1:1 as possible). Each of the combined protein fractions was precipitated with 4 volumes ice-cold acetone overnight. The
precipitated proteins were re-dissolved in SDS-loading buffer, containing 10 mM DTT and incubated for 30 min at 55 ℃. Following reduction, the samples were alkylated by iodoacetamide in a total concentration of 55 mM. The samples were incubated in the dark for 20 min, and then heated at 70 ℃ before they were loaded on a Nu-PAGE gel for SDS-PAGE separation. A constant voltage of 200 V was applied over the gel for 55 min. After separation, the gels were stained with Coomassie brilliant blue, and destained with 10% (v/v) methanol and 10% (v/v) acetic acid. The gels were rinsed in water for at least 2 h. The proteins were subjected to in-gel digestion as described previously (Shevchenko et al., 2006). Briefly, each gel lane was divided into ten gel slices according to intensity, and digested with sequence-grade porcine trypsin overnight in 10 mM ammonium bicarbonate containing 10% (v/v) acetonitrile. The digestion reaction was quenched by adding 1:2 (v/v) 5% formic acid/acetonitrile with an incubation period for 15 min at 37 ℃. The supernatant was transferred to new 0.5 ml tubes, and dried in a vacuum centrifuge. The peptides were dissolved in 0.1% Trifluoroacetic acid (TFA), and desalted on C-18 STAGE-tips (Rappsilber et al., 2003).
2.4. Mass spectrometric analysis
The peptides were eluted from the STAGE-tips with 0.1% trifluoroacetic acid/acetonitrile (1:2), dried in a vacuum centrifuge, and solved in 1% trifluoroacetic acid/2% aceto- nitrile. Following desalting, the peptides were applied on a Dionex Ultimate 3000 nano-LC system (Dionex, Sunny- vale, CA) connected to a linear quadrupole ion trap - Orbi- trap (LTQ-Orbitrap) mass spectrometer (Thermo Electron, Bremen, Germany) equipped with a nano-electrospray ion source. An Acclaim PepMap 100 column (C18, 3 um, 100 Å) (Dionex) with a capillary of 12 cm bed length was used for separation by liquid chromatography. The samples were eluted with a flow rate of 0.3 uL/min for the nano-column, and the solvent gradient used was 7-40% of aqueous 90% acetonitrile with 0.1% formic acid (solvent B) in 90 min fol- lowed by a wash step of 40-80% solvent B in 8 min. Solvent A was aqueous 2% acetonitrile with 0.1% formic acid. The mass spectrometer was operated in the data-dependent mode to automatically switch between Orbitrap-MS and LTQ-MS/MS acquisition. Survey full scan MS spectra (from m/z 300 to 2000) were acquired in the Orbitrap with resolution R = 60,000 at m/z 400 (after accumulation to a target of 1,000,000 charges in the LTQ). The method used allowed sequential isolation of the most intense ions, up to six, depending on signal intensity, for fragmentation on the linear ion trap using collisional induced dissociation (CID) at a target value of 100,000 charges.
2.5. Bioinformatics
The MaxQuant software suite (ver. 1.0.13.13. maxquant. org) was utilized for data processing of the raw files into MS and MS/MS peak lists, and assignment of isotope patterns into SILAC-pairs as described (Cox and Mann, 2008). SILAC-pairs consist of peptide peaks from both the heavy and the light protein, and are used to calculate the heavy to light-ratio (H/L-ratio) used for the quantification.
The peak lists were submitted to our in-house MASCOT database (ver. 2.2. Matrix Science), and were searched against the IPI_human database (v3.52) via MASCOT Daemon (ver. 2.2.2. Matrix Science). The database was supplemented with additional sequences for common contaminants, and, in addition, contained the reversed sequence of each entry to aid in the controlling of false- positives. The following parameters were used for search- ing: Enzyme specificity: Trypsin; Maximum missed cleav- ages: 2; fixed modifications: Carbamidomethyl (Cys); variable modifications: N-acetyl (Protein), Oxidation (Met), and Deamidated (NQ); MS/MS mass tolerance: 0.5 Da; MS tolerance: 7 ppm. After identification of proteins by data- base search, MaxQuant was utilized to assemble the peptides into proteins, and quantify the proteins, followed by statistical validation of the results. The p-values reported by MaxQuant were corrected for multiple comparisons using the Benjamini-Hochberg correction (Benjamini et al., 2001). Proteins were included in the final table if the following criteria was fulfilled: protein False discovery rate (FDR) of less than 0.01; peptide FDR less than 0.05; proteins identified with at least two peptides, of which at least one is unique; peptides contain at least 6 amino acids; the corrected p-value was less than 0.05 (5%); each considered regulated protein was present in at least two of the three replicates. Proteins identified on the basis of one unique peptide were included if Benjamini-Hochberg corrected p- values were less than 0.001, and the protein was identified in at least two of the three parallels.
2.6. Systems biology
Ingenuity Pathway Analysis (IPA; http://www.ingenuity. com) was used for pathway and biological function analysis
of differently expressed proteins/genes. The data set from the proteomic experiment was translated into HUGO gene identifiers and uploaded to IPA. The IPA software is a Java- based online exploratory tool with a curated database for genes, and millions of published literature references. The IPA database is used to integrate genomics, transcriptomics and proteomics data with mining techniques to predict and build gene networks, pathways, and biological function clusters. The IPA software maps the biological relationship of uploa- ded genes according to published literature in the database. The output results are based on numbers of uploaded genes in the cluster or network and the size of network or cluster in the Ingenuity knowledge database, and include scores and p- values used for validation of the results. Fisher’s exact test is used to determine the probability that each biological func- tion is due to chance alone. Scores for IPA networks are the negative logarithm of the p-value. They indicate likelihood of the focus proteins in a network being found together due to random chance. Scores of 2 or higher have at least a 99% likelihood of not being generated by chance alone.
3. Results
3.1. Proteomic analysis of H295R cells exposed to ZEN
The proteomic analysis of the H295R cell line exposed to ZEN was performed with a combined 1D GEL-LC-MS/MS approach resulting in a total of 20 identified regulated proteins, as shown in Table 1. At non-cytotoxic, low concen- tration of mycotoxin, we see a significant, but not massive change in the regulation of the proteome. Several interesting regulated proteins with estrogenic properties, like e.g. several regulated cytochrome-c oxidase proteins were identified (Ding et al., 2006; Pochapsky et al., 2010; Vyhlidal et al., 2002).
| Protein names | Uniprot acc. | Mw [kDa] | Ratio H/L | Function/process | Abbreviation |
|---|---|---|---|---|---|
| Activating molecule in BECN1-regulated autophagy protein 1 | Q9C0C7 | 142 | 0.084 | Autophagy; cell differentiation | AMBRA1 |
| Ankyrin repeat domain-containing protein 27 | Q96NW4 | 117 | 4.693 | GTPase activator activity | ANKRD27 |
| Cytochrome b-c1 complex subunit 1, mitochondrial | P31930 | 53 | 0.660 | Metalloendopeptidase activity | UQCRC1 |
| Cytochrome b-c1 complex subunit 2, mitochondrial | P22695 | 48 | 0.643 | Oxidative phosphorylation | UQCRC2 |
| Cytochrome-c oxidase subunit 2 | P00403 | 25 | 0.645 | Cytochrome-c oxidase activity; heme binding | MT-CO2 |
| Cytochrome-c oxidase subunit 4 isoform 1, mitochondrial | P13073 | 20 | 0.635 | Cytochrome-c oxidase activity | COX4I1 |
| Cytochrome-c oxidase subunit 5B, mitochondrial | P10606 | 14 | 0.611 | Cytochrome-c oxidase activity | COX5B |
| DNA-binding protein RFX7 | Q2KHR2 | 147 | 0.215 | DNA-binding | RFX7 |
| Heat shock protein 90 kDa beta member 1 | P08238 | 83 | 0.089 | Nitric-oxide synthase | GRP94c |
| (Heat shock protein 94c) | regulator activity | ||||
| Hepatoma-derived growth factor | P51858 | 27 | 0.571 | DNA-binding | HDGF |
| Lymphoid-restricted membrane protein (JAW1) | Q12912 | 62 | 0.041 | Vesicle fusion | LRMP |
| Mitochondrial import receptor subunit TOM40 homolog | O96008 | 38 | 0.640 | Protein targeting to mitochondrion | TOMM40 |
| Patched domain-containing protein C6orf138 | Q6ZW05 | 96 | 16.896 | Hedgehog receptor activity | C6orf138 |
| Protein Shroom3 | Q8TF72 | 217 | 0.103 | Cell morphogenesis species; skeletal system development | SHROOM3 |
| Annexin A2 | B2R657 | 53 | 0.206 | Calcium ion binding | ANXA2 |
| Ribosomal protein L29 (RPL29) pseudogene | Q5T1D1 | 17 | 0.444 | Translation | RPL29 |
| Scavenger receptor class B member 1 | Q8WTV0 | 61 | 1.364 | Cell adhesion | SCARB1 |
| Synembryn-B | Q9NVN3 | 59 | 0.093 | Regulation of G-protein coupled receptor protein signaling pathway | RIC8B |
| Transcription factor BTF3 homolog 4 | Q96K17 | 17 | 0.656 | BTF3L4 | |
| Trypsin-3 | P35030 | 33 | 0.150 | Digestion; endothelial cell migration | PRSS3 |
3.2. Protein interaction network analysis
To aid the exploration of data and interpretation of the regulated proteins, a systems biology approach was used. Data were analyzed by use of Ingenuity Pathways Analysis (IPA, Ingenuity® Systems, www.ingenuity.com). IPA was used to generate a graphical view of the molecular connections between the regulated proteins identified in this study and aid the understanding of the identified proteins in the context of biological pathways, functions, and cellular processes (Figs. 3-5).
The relationships between the regulated proteins indi- cate a limited and specific induction caused by ZEN. A high degree of relation and connectivity in this context indicate that the identified proteins are part of the same reactions and pathways. In the top scoring network 1 (Fig. 3), the data centers around the transcription factor Nuclear Factor kappa B signaling cascade (NF-KB) that controls a number of important cellular functions like inflammation, stress response, and cardiovascular growth (molecules added by IPA, not present in the original data set are called white nodes) (Brasier, 2006). In addition, there is a connection with a cytochrome related cluster of proteins including the cytochrome-c oxidase proteins cytochrome-c oxidase subunit 2, mitochondrial (MT-CO2), cytochrome-c oxidase subunit 5B, mitochondrial (COX5B), and cytochrome-c oxidase subunit 4 isoform 1, mitochondrial (COX4I1). A connection between NF-KB and scavenger receptor class protein B (SCARB1) and hepatoma-derived growth factor (HDGF) is also observed. Furthermore, annexin A2 (ANXA2)
is a central molecule in the network involved in important cell functions like e.g. regulation of NF-KB and iron metabolism (Munoz et al., 2011; Sarkar et al., 2011).
The second network, shown in Fig. 4, centers on the v- erb-b2 erythroblastic leukemia viral oncogene homolog 2 (ERBB2). ERBB2 connects with protein SHROOM3 (SHROOM3), trypsin-3 (PRSS3), heat shock protein 90 kDa beta (HSP90B1) and microtubule-associated protein 1B (MAP1B) molecules. In addition, microRNA 331 (MIR331) connects patched domain-containing protein C6orf138 (C6ORF138), synembryn-B (RIC8B) and DNA-binding protein RFX7 (RFX7). Moreover, the mitochondrial cyto- chrome b-c1 complex subunits form a small cluster with proteins of assumed similar function.
3.3. Canonical pathways
Canonical pathway analysis is complementary to the functional analysis output generated by IPA. The regulated proteins identified in this work are parts of two known canonical pathways: the mitochondrial dysfunction pathway and the oxidative phosphorylation pathway (supplementary data).
4. Discussion
4.1. Identified regulated proteins
Although ZEN is known to cause apoptosis and cyto- toxicity (Abid-Essefi et al., 2003; Gazzah et al., 2010), using
TOMM40
F2RL3
Cytochrome bc1
SCARB1
HDGF
PRSS3 (includes EG:5646)
F2
FP
UQCRE2
UQCRC1
NFKB (complex)
ITPR3
COX6B1
ERP29
Veg
COX7C (includes EG:1350)
MT-CO2
COX6C
HTT
HAMP
HRAS
RM
COX414
Cytochrome c oxidase
COX7AZ
GAPDH (includes EG:14433)
AL
ANXA2
COX58
iron
MT-CO3
RPL29 (includes EG:6159)
RPL27A
ADAMTS5
ENAM
C6ORF138
RFX
TNC
Alpha catenin
SHROOM3
SFN
MIR331 (includes EG:100313975) 4
RIC8B
CTNNB
TNFSFTO
CSNK2A1
MIR125A (includes EG:406910) 4
MAP1B
CSNK2A2
NEK9 (includes EG:91754)
ERBB2
XBP1
PDIA4
NRG1
CALR
AURKA
TCEB1
P90B1
TUBB4
VWF
AMBRA1
AR
ASGR2
Isp90
CUL4B
MIR154 (includes EG:406946) 4
RXRB
SMARCC1
BTF3L4
GTF2F2
a low concentration of ZEN identifies the regulated proteins and reflects the change induced by the mycotoxin, specif- ically. No massive stress response is initiated, as seen by the limited number of regulated stress proteins. A few of the identified proteins fit the category of stress proteins, but these proteins have general functions relating to protein folding etc. and agrees with the previously observed changes induced by similar drugs. As shown by the IPA- analysis, the identified proteins are highly connected. The use of IPA to manually add molecules to the networks also allowed for the identification of endocrine affecting, and endocrine disrupting properties of ZEN.
The highest scoring network (Fig. 3) from the IPA- analysis show the signaling cascade NF-KB is central, and connects to both hepatoma-derived growth factor (HDGF) and scavenger receptor protein class B member 1 protein (SCARB). NF-KB is involved in expression of HDGF mRNA (Hinata et al., 2003). HDGF was found down-regulated in our study, and blocking of HDGF is shown to induce apoptosis in colon carcinoma cells (Liao et al., 2010). SCARB, which is up-regulated, decreases activation of human NF- KB complex(es) in Hek cells (Guo et al., 2009). Moreover, it
is possible that ZEN influences the production of androgens (e.g. testosterone) through the up-regulation of SCARB (the mechanism is not completely understood, Fenske and Fink- Gremmels, 1990). The SCARB has previously been assigned a role in estrogenicity as it is found that Rat SCARB1 is involved in biosynthesis of androgen (Wu et al., 2003), and that knockout mutation of the scarb1 gene reduces the quantity of progesterone in mouse plasma with 50% (Jimenez et al., 2010). The serine protease PRSS3 that is found to promote tumor growth in pancreatic cancer is observed to be down-regulated because of ZEN stimulation. This suggests that ZEN can be a potential drug target candidate for pancreatic cancer (Jiang et al., 2010).
The second most significant network places ERBB2 as the central player. ERBB2 belongs to a receptor tyrosine kinase family important in e.g. cell division, death, and motility adhesion, and plays a key role in the development of cancer (Yarden and Sliwkowski, 2001). The erbB2 gene is often found to be over-expressed in breast cancer, and is linked with high mortality and frequency of relapse (Slamon et al., 1987). ERBB2 is proposed to block the onset of apoptosis induced by e.g. Taxol (Yu et al., 1998). ERBB2
COX411
RPL29 (includes EG:6159)
Cytochrome c oxidase
COX5B
ANXA2
MT-CO2
ESR1
Hsp90
NCOA2
HSP90B1
TNF
MYC
SCARB
NCOA3
TF
L1B
ESR2
Cytochrome bc1
IL6
zearalenone
CONDI
Sod
Histone h3
UQCRC1
UQCRC2
KITLG
SOK2
COKNIB
CCNB1
interacts with the SHROOM3 protein, which is part of a protein family associated with the actin cytoskeleton, and is involved in shaping the cell (Fig. 4) (Hagens et al., 2006). ERBB2 also interacts with HSP90B1 (Fig. 4), as the two proteins are found to physically interact and bind (Chavany et al., 1996). The heat shock proteins are involved in many cell processes including the folding of proteins and general stress responses (Lindquist and Craig, 1988). Heat shock 90 proteins are proposed as anti-cancer targets, and the observed down-regulation indicates an inhibitory effect exerted by ZEN (Hahn, 2009). In addition, the HSP90B1 proteins have been reported regulated by other estrogenic chemicals (Fuqua et al., 1989; Mendelsohn et al., 1991; Papaconstantinou et al., 2001).
The proteins cytochrome bc1, cytochrome b-c1 complex subunit 1, mitochondrial (UQCRC1) and cytochrome b-c1 complex subunit 2, mitochondrial (UQCRC2) are part of the respiratory complex III and involved in the electron trans- port chain and oxidative phosphorylation. These two pathways are shown to be the most affected by ZEN stim- ulation (discussed later). Together, these down-regulated proteins form two clusters as shown in Figs. 3 and 4.
RFX7, RIC8B, and C6ORF138 are connected via MIR331, a small non-coding microRNA molecule known as post- transcriptional regulator of gene expression (Lagos- Quintana et al., 2001). All three proteins are predicted targets for regulation by MIR331 (John et al., 2004). RFX7 is part of the regulatory factor X (RFX) genes found implicated
in several serious illnesses like e.g. major histocompati- bility complex (MHC) class II deficiency (Aftab et al., 2008). RFX7 is one of the latest addition to the gene family, and there are limited literature available describing the precise mode of action and function for this gene. Another inter- esting observation is that RIC8B is associated with the expression of odorant receptors (Zhuang and Matsunami, 2007), and it is known that estrogen and testosterone have an effect on odor detection and olfactory perception and this is a further link to estrogenicity of ZEN (Good et al., 1976; Pietras and Moulton, 1974). Interestingly, the autophagy/beclin-1 regulator 1 (AMBRA1), another central molecule, is shown to be a molecular target of 17ß-estradiol (Van Dorst et al., 2011). As a phytoestrogen, the binding of AMBRA1 and ZEN is not unexpected, but little is known of the role of this interaction.
Although there are few proteomic experiments on the H295R cell line to compare, Stigliano et al. (2008) identified 29 proteins using a 2D-gel approach for cells induced with mitotane that disrupts steroidogenesis and alters the pro- teomic profile. As with mitotane, ZEN also seems to affect stress response (e.g. HSP90B1; although minimal with ZEN), cytoskeleton (e.g. SHROOM3), energy modulation (e.g. MT-CO2, COX5B, COX4I1), and tumorigenesis (e.g. PRSS3). In addition, we also find proteins implicated in iron metabolism (TF), apoptosis (HDGF), and observe that the mitochondrial dysfunction and oxidative phosphorylation pathways are affected.
4.2. Targeted approach for creating networks
An in silico analysis of the most common proteins known to interact with ZEN was undertaken using IPA. As expected, the results show proteins involved in estro- genicity and endocrine disruption (Table 2, the 15 highest scores). The proteins that were observed to be regulated in our work were then connected with the proteins in Table 2 using IPA (Fig. 5). The results show strong correlations between the regulated proteins and the most well-known ZEN interacting proteins.
One example is estrogen receptor 2 that stimulates down-regulation of the human transcription factor c-Myc mRNA in several cell lines (Hartman et al., 2009). c-Myc is an early response gene with important regulatory functions in the cell cycle, and known to be regulated in colonic cancer (Sikora et al., 1987) in addition to being one of the most common oncogenes in breast cancer (Blancato et al., 2004). c-Myc is previously observed to be down-regulated in human breast tissue by a .- ZOL and its use has been proposed as treatment of climacteric symptoms (Deng et al., 2009). c- Myc is also known to stimulate the expression of the rat homolog of SCARB1 (Guo et al., 2000) (discussed previ- ously), found to be up-regulated here. Moreover, it is observed that the SCARB1 gene contains estrogen response elements, on which both ESR1 (ER-a) and ESR2 (ER-B) may bind (Lopez et al., 2002). It is also shown that ZEN and metabolites bind to several estrogen receptors (a and B) (Kuiper et al., 1998). The human ANXA2 (down-regulated in our study) is found to bind c-Myc mRNA, and stimulate up- regulation of human c-Myc protein (Filipenko et al., 2004). These examples show that ZEN influences important regu- latory proteins, and that the cascade of changes induced by ZEN is not limited to estrogen receptor mediated changes, but also affects proteins related to several types of cancer.
4.3. Canonical pathways
The two main canonical pathways with the highest significance identified by IPA are the mitochondrial
| Name in figure | Gene name |
|---|---|
| CCND1 | Cyclin D1 |
| CCNB1 | Cyclin B1 |
| CDK2 | Cyclin-dependent kinase 2 |
| CDKN1B | Cyclin-dependent kinase inhibitor 1B (p27, kip1) |
| ESR1 | Estrogen receptor 1 |
| ESR2 | Estrogen receptor 2 |
| Histone h3 | Histone h3 |
| IL1B | Interleukin 1, beta |
| IL6 | Interleukin 6 (interferon, beta 2) |
| KITLG | KIT ligand |
| MYC | v-myc myelocytomatosis viral oncogene homolog (avian) |
| NCOA2 | Nuclear receptor coactivator 2 |
| NCOA3 | Nuclear receptor coactivator 3 |
| Sod | Copper-zinc superoxide dismutase |
| TNF | Tumor necrosis factor |
dysfunction-, and the oxidative phosphorylation pathways. UQCRC1, UQCRC2, COX5B, COX4I1 and MT-CO2 proteins, observed to be regulated, are involved in both canonical pathways. There have been reports stating the influence of ZEN on these pathways, but the precise actions, and proteins involved, have yet to be determined.
The oxidative phosphorylation is important as reactive oxygen species (ROS) formation is believed to be one of the mechanisms of ZEN-mediated cytotoxicity (Ferrer et al., 2009). ROS is produced in several non-malignant processes, but excess may cause disturbances in metabolic pathways, and DNA damage. There are several studies linking ZEN to oxidative stress (Hassen et al., 2007; Marin et al., 2010). Furthermore, ß-ZOL and ZEN have been shown to induce an increase in O2 synthesis and a decrease in production of interleukin (IL)-8 (Marin et al., 2010). As IL-8 is a well-known mediator of inflammation, this result is rather puzzling. This dichotomy is well known as reports find ZEN to work as both suppressors and inductors of the productions of cytokines (Ben Salah-Abbes et al., 2008; Ruh et al., 1998). It must be taken into consideration that these results are in the neutrophil cell model and that the results cannot be compared directly to our own results, but only be used as a general comparison. A study where by the bovine mammary gland was stimulated with estrogen also resulted in regulation of the oxidative phosphorylation pathway (Li and Capuco, 2008). Oxidative phosphorylation is of upmost importance in the reloading of ADP into ATP, and this change is contributed to the ability of estrogen to increase mito- chondrial efficiency and transcription. This shows that the metabolic effects of estrogen and the phytoestrogen ZEN both affect the oxidative phosphorylation pathway.
5. Conclusions
The data presented here represent the first published work investigating the effects of zearalenone on the pro- teome of the adrenocortical cell line H295R using a metabolic labeling strategy. The exposure of H295R cells to ZEN yielded 21 regulated proteins that were grouped into mainly two networks centering around signaling cascade NF-KB and the oncogene ERBB2, respectively. In addition, several proteins known to play a role in estrogenic disorders were regulated. The information provided by these experiments helps eluci- date the complex endocrine effects of ZEN, in addition to indicating ZEN and metabolites as potential therapeutic templates for creating novel drugs targeting cancer.
Conflict of interest statement
There are no competing interests.
Appendix. Supplementary data
Supplementary data associated with this article can be found, in the online version, at doi: 10.1016/j.toxicon.2011. 08.015.
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