induced by hepatotoxicants on primary cultures of rat hepatocytes

Use of a low-density microarray for studying gene expression patterns

induced by hepatotoxicants on primary cultures of rat hepatocytes

F. de Longuevillea*, F.A. Atienzarb, L. Marcqa, S. Dufraneb, S. Evrarda, L. Woutersb, F. Lerouxb,

V. Bertholeta, B. Gerinb, R. Whomsleyb, Arnould Tc, J. Remaclea and M. Canningb

a Eppendorf Array Technology (EAT), 20, rue du séminaire, 5000 Namur, Belgium

b UCB SA Pharma sector, Chemin du Foriest, B-1420 Braine-l’Alleud, Belgium

c Laboratory of Biochemistry and Cellular Biology, University of Namur, Belgium

* To whom all correspondence should be addressed:

Françoise de Longueville, Eppendorf Array Technology (EAT), 20, rue du séminaire, 5000 Namur, Belgium.

Phone: +32-81-72 56 15 and Fax: +32-81-72 56 23

E-mail: [email protected]

Section: original research articles

Short title: gene expression profiling using low-density microarray

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Copyright (c) 2003 Society of Toxicology

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Abstract

In the field of gene expression analysis, DNA microarray technology is having a major

impact on many different areas including toxicology. For instance, a number of studies have

shown that transcription profiling can generate the information needed to assign a compound

to a mode-of-action class. In this study, we investigated whether compounds inducing similar

toxicological endpoints produce similar changes in gene expression. In vitro primary rat

hepatocytes were exposed to 11 different hepatotoxicants: acetaminophen, amiodarone,

clofibrate, erythromycin estolate, isoniazid, α-naphtylylisothiocyanate, β-naphtoflavone, 4-

pentenoic acid, phenobarbital, tetracycline and zileuton. These molecules were selected on the

basis of their variety of hepatocellular effects observed such as necrosis, cholestasis, steatosis

and induction of CYP P450 enzymes. We used a low-density DNA microarray containing 59

genes chosen as relevant toxic and metabolic markers. The in vitro gene expression data

generated in this study were generally in good agreement with the literature which mainly

concerns in vivo data. All the tested drugs generated a specific gene expression profile. Our

results show that even with a relatively limited gene set, gene expression profiling allows a

certain degree of classification of compounds with similar hepatocellular toxicities such as

cholestasis, necrosis, ... The clustering analysis revealed that the compounds known to cause

steatosis were linked, suggesting that they functionally regulate similar genes and possibly act

through the same mechanisms of action. On the other hand, the drugs inducing necrosis and

cholestasis were pooled in the same cluster. The drugs arbitrarily classified as the CYP450

inducers formed individual clusters. In conclusion, this study suggests that low-density

microarrays could be useful in toxicological studies.

Key words: hepatotoxicants; gene expression pattern; low-density microarray; toxicogenomics; drug

metabolism.

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Abbreviations:

Ah = Aryl hydrocarbon, ANIT = α-naphtylylisothiocyanate, AM = amiodarone, APAP =

acetaminophen, BNF = β-naphtoflavone, CAR = constitutive androstane receptor, CLO = clofibrate,

CT = threshold cycle, CYP = cytochrome, DMF = dimethyl formamide, DMSO = dimethyl

sulfoxide, EC50 = concentration producing 50% change, EGTA = ethylene glycol-bis-(β-aminoethyl

ether)-N,N,N’,N’-tetraacetic acid) ERY = erythromycin estolate, GADD = growth arrest and DNA

damage, GST = glutathione S-transferase, HBSS = Hank’s balanced salt solution, Hdac = histone

deacetylase, HGPT = hypoxanthine guanine phosphoribosyl transferase, HKG = Housekeeping

gene, HMG = 3-hydroxy-3methylglutaryl, HO-2 = heme oxygenase 2, ISN = isoniazid, MDR =

multi-drug resistance, MnSOD = manganese superoxide dismutase, MTT = 3-(4,5-dimethylthiazol-2-

yl)-2,5-diphenyltetrazolium bromide, PB = phenobarbital, PCNA = proliferation cellular nuclear

antigen, ODC = ornithine decarboxylase, PBS = phosphate buffered saline, SMP30 = senescence

marker protein-30, UDPGT = UDP-glucuronosyltransferase, WEC = William’s E medium

supplemented with L-glutamine, penicillin, streptomycin and foetal bovine serum, WDI = William's E

medium supplemented with L-glutamine, penicillin, streptomycin, insulin and dexamethasone.

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1. Introduction

With increasing costs of new drug development, there is a crucial need to conduct toxicity

evaluation as early as possible and on as many potential chemical leads as feasible. During the

drug developmental process, undesired toxicity accounts for about one third of compound failures

(Johnson and Wofgang, 2000). Therefore, it is clear that new powerful technologies are needed as

an alternative to classical toxicological tests for a rapid screening. Since some recent studies have

shown the usefulness of DNA microarrays in toxicological studies, the scientific community is

showing a growing interest for this kind of technology. The emerging field of “toxicogenomics”

could be defined as the study of toxicological processes at the transcriptome level of a target

organ or cell. It seems that DNA microarrays could be very helpful not only to predict drug

induced toxicity but also to better understand mechanisms of actions of drugs (Fielden and

Zacharewski, 2001; Storck et al., 2002). In this context, gene expression microarrays could help

to prioritize lead compounds.

DNA microarrays consist of DNA fragments corresponding to genes. The use of high

density microarrays containing thousands of DNA fragments has the main advantage that the

expression level of a large number of genes can be studied simultaneously. However, the major

drawbacks are related to the high cost and the time taken for analysis and interpretation of the

data. Low-density microarrays, even though they contain fewer genes, can still offer the ability to

rapidly study gene expression changes following chemical exposure (de Longueville et al., 2002).

However, it is clear that with low-density microarrays, the effects on genes not selected will

obviously be missed.

While it may take weeks, months or even years before some traditional toxicological

endpoints occur, specific changes in mRNA levels could occur within a few hours or days after

exposure to chemical compounds. Toxicogenomics builds upon the fact that relevant toxicological

outcomes are preceded by such changes in gene expression. A recent study revealed a strong

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correlation between the histopathology, clinical chemistry and gene expression profiles induced

by 15 different known hepatotoxicants (Waring et al., 2001b). In addition, comparison of gene

expression profiles induced by new drugs with those induced by known toxicants obtained in a

database could help identify and predict potential toxicities (Hamadeh et al., 2002a). Recent

studies have also shown that not only gene expression analysis reveals chemical specific profiles

(Hamadeh et al., 2002b) but also that compounds belonging to a same class of toxicant yield to

similar gene expression patterns that are distinct from other profiles generated by other class of

chemicals (Bartosiewicz et al., 2001; Morgan et al., 2002).

While the ultimate goal of toxicogenomics is to generate safe drugs for human, the

majority of studies are performed on rodents despite the fact that the human predictability of

standard rodent tests shows only 45% concordance (Johnson and Wolfgang, 2000). However,

primary hepatocytes are well suited for toxicogenomic studies because they display a certain level

of metabolic activity and the liver is a major stage for toxic events (Waring et al., 2001a).

Hepatotoxicity is a common reason for withdrawal of compounds from the market (Baker et al.,

2003). In addition, the use of cell culture models reduces the animal utilization and need for the

synthesis of new compounds on a large scale (Baker et al., 2001). However, it is also clear that

there are a number of limitations in using in vitro approaches such as the functional differences

observed in primary hepatocytes relative to the intact liver, the absence of interactions with

biological entities (e.g. organs, blood) under in vitro conditions, the difficulty to select doses and

time points which are representative of an in vivo situation.

Compared to the input of drug developers in toxicogenomics, the number of published studies

on toxicogenomic involving the analysis of several compounds is still limited and mainly

restricted to high-density microarrays (Burczynski et al., 2000; Bulera et al., 2001; Gerhold et al.,

2001; Waring et al., 2001a; 2001b; Hamadeh et al., 2002a; 2002b; de Longueville et al., 2002). In

this study, we have used a low-density microarray containing 59 genes to analyze gene expression

profiles generated in primary cultures of rat hepatocytes exposed to 11 different hepatotoxicants.

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These latter were pooled into 4 groups labeled: necrosis [isoniazid (ISN) and acetaminophen

(APAP)], cholestasis [erythromycin estolate (ERY) and α-naphtylylisothiocyanate (ANIT)],

steatosis [tetracycline, 4-pentenoic acid and amiodarone (AM)], and induction of cytochromes

P450 (CYP P450) subfamilies [clofibrate (CLO), β-naphtoflavone (BNF), phenobarbital (PB),

and zileuton].

The aims of this study were to analyze changes in gene expression levels induced by in

vitro primary hepatocytes exposed to different xenobiotic treatments and to determine if gene

expression profiles generated with a low-density microarray would permit a classification of

compounds associated signatures.

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2. Materials and methods

2.1. Rat Hepatocyte Isolation

Wistar rats, 7-8 weeks old on the day of sacrifice, were obtained from Iffa Credo

(L'Arbresles, France). Upon arrival and for the duration of the acclimatization period, animals had

free access to UV-treated water and controlled rodent diet (Dietex, Witham, UK). The animal

room temperature was maintained between 20 and 24°C with a relative humidity of 40 to 70%.

The light cycle was 12 h of light and 12 h of darkness.

Rats fasted for 24 h were anesthetized with an intraperitoneal injection of sodium

pentobarbital (Pharmacie du Val d'Hony, Esneux, Belgium) as a saline solution (80 mg/kg)

before liver perfusion and hepatocytes were isolated using a modification of Seglen’s two step

collagenase perfusion technique (Seglen, 1976). A laparotomy was performed and a catheter

was introduced into the portal vein, allowing the perfusion of the liver in situ at 37°C, with

Ca2+ and Mg2+ free HBSS (Hank’s balanced salt solution; BioWhittaker Inc, Walkersville, MD,

USA), supplemented with 0.47 mmol/l EGTA [ethylene glycol-bis-(β-aminoethyl ether)-

N,N,N’,N’-tetraacetic acid; Sigma, St-Louis, MO, USA] and 33.5 mmol/l NaHCO3 (J.T.

Baker, Deventer, Holland). The pH of this solution was kept at 7.4 with permanent bubbling

of sterile carbogen (5% CO2, 95% O2; Air Liquide Medical, Machelen, Belgium). A second

catheter was introduced into the right atrium of the heart, permitting the recycling of the

perfusion medium. After a few minutes perfusion, 90 U/ml collagenase “Hepatocytes

qualified” (Invitrogen, Carlsbad, CA, USA) and 1.5 mmol/l CaCl2 (Sigma, St-Louis, MO,

USA) were added to the perfusion medium. After 10 min of perfusion, the liver was removed

and the cells were dissociated, filtered and washed in WEC (William’s E medium

supplemented with 2 mmol/l L-glutamine, 100 U/ml penicillin, 100 µg/ml streptomycin and

10 % v/v foetal bovine serum (Invitrogen, Carlsbad, CA, USA). Hepatocytes number and

viability were assessed by counting unstained and stained cells, after addition of trypan blue

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dye (Invitrogen, Carlsbad, CA, USA), using a Burker haemocytometer. The cell suspension

was considered to be valid and used when the cell viability was greater than 80 %.

2.2 Cytotoxicity assessment

To ensure that sublethal concentrations of test compounds were used in the experiment

to establish the gene expression profile, the cytotoxicity was assessed in vitro, on freshly

isolated male adult rat hepatocytes using the MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-

diphenyltetrazolium bromide] reduction method (Otoguro et al., 1991).

2.2.1 Compound solutions

The different compounds were freshly dissolved at 100-fold the final concentrations

either in DMSO (dimethyl sulfoxide; ICN Biomedical, Eschwege, Germany) (all compounds

except CLO and PB), in DMF (dimethyl formamide; Sigma, St-Louis, MO, USA) (CLO) or

in H2O (PB). Each solution or corresponding vehicle was then diluted 100-fold in WDI

[William's E medium (Invitrogen, Carlsbad, CA, USA)] supplemented with 2 mmol/l L-

glutamine (Invitrogen, Carlsbad, CA, USA), 100 U/ml penicillin (Invitrogen, Carlsbad, CA,

USA), 100 µg/ml streptomycin (Invitrogen, Carlsbad, CA, USA), 10 nmol/l insulin (Sigma, St-

Louis, MO, USA) and 10 mmol/l dexamethasone (ICN Biomedical, Eschwege, Germany) to

obtain the desired final concentrations (Table 1).

2.2.2 Hepatocyte incubations and MTT reduction assay

Freshly isolated hepatocytes were seeded in collagen S-precoated 24-well plates

(Becton Dickinson, Franklin Lakes, NJ, USA) at a density of 105 viable cells/cm2 for 3 h at

37°C under a 5% CO2/95% humidified atmosphere in WEC (William’s E medium

supplemented with 2 mmol/l L-glutamine, 100 U/ml penicillin, 100 µg/ml streptomycin and

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10% v/v foetal bovine serum (Invitrogen, Carlsbad, CA, USA)). After cell attachment, the

medium was replaced with 1 ml of compound or vehicle solution, and the cells were

incubated for a further 24 h before endpoint measurement. Compounds at the different

concentrations and vehicles were tested in triplicates. Male rat hepatocytes were incubated

with all the compounds while female rat hepatocytes were only incubated with

acetaminophen.

The cytotoxicity was then assessed using the MTT reduction method. The test medium

was discarded and fresh medium containing 1 mg/ml MTT (Sigma, St-Louis, MO, USA) was

added to the monolayers. After a 3 h incubation at 37°C in a humid atmosphere (5% C02: 95%

air), the medium was removed and the formazan dye formed by succinyl dehydrogenase-catalysed

reaction solubilized with isopropranol. The absorbance was then measured at 550 nm using a

microtiter plate reader SpectramaxPlus (Molecular Devices Corporation, Sunnyvale, CA, USA).

The toxic effect of each compound at the different concentrations was expressed as the

percentage of the absorbance determined for control cells incubated with the corresponding

vehicle. The concentration that produces a change of 50% (EC50) in this endpoint assay was

calculated by non-linear iterative adjustment using the Levenburg Marquardt algorithm (XL fit

Windows from Molecular Devices, Sunnyvale, CA, USA).

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2.3 Gene expression analysis

In order to evaluate the reliability of microarray experiments and to obtain an accurate gene

expression profile for each compound, three independent hepatocyte preparations were carried

out for each compound. A microarray experiment was performed on each hepatocyte

preparation. The microarray experiment included the following steps; mRNA extraction,

labeled cDNA synthesis and microarray hybridization. In summary, three hybridization on

microarray were performed for each compound (n=3).

2.3.1 Cell treatments

It has to be noted that for each drug and its control, the same hepatocyte preparation

was used. In addition, the effect of each hepatotoxicant was tested on three independent

hepatocyte preparations originating from three different rats.

Cells were seeded at 105 cells/cm2 in collagen S-precoated 75 cm2 flasks using WEC

medium (15 ml/flask) (Invitrogen, Carlsbad, USA). Hepatocytes were then incubated at 37°C

under a 5% CO2/95% humidified atmosphere and allowed to attach for 3 h prior to the

incubation with the reference compounds. The culture media was then replaced with WDI

culture medium containing one of the test compounds or vehicle alone control (DMSO or

DMF) (Sigma, St-Louis, MO, USA) (Table 1). Fresh stock solutions of compounds were

prepared at 100 fold the final concentration in DMSO (all compounds except CLO), at 400

fold the final concentration in DMF (CLO). The control cell medium was prepared by diluting

the vehicle in WDI to reach a final concentration of 1% v/v for DMSO or 0.25 % v/v for

DMF (Sigma, St-Louis, MO, USA). The rat hepatocytes cultures were then incubated for a

further 24 h. At the end of the treatment period, hepatocytes were washed twice with PBS at

37°C. Cells were then stored at -80°C until mRNA extraction.

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2.3.2 mRNA isolation

mRNA was isolated using the KingFisherTM mRNA extraction kit according to the

manufacturer’s protocol (Thermo Life Sciences, Brussels, Belgium). Cells were lysed at room

temperature for 15 min. Cell lysates were centrifuged twice on Qiashredder columns

(Westburg, Leusden, The Netherlands) at 14000 rpm for 2 min. mRNA extraction was then

performed on non-viscous lysate with the KingFisher mlTM device. mRNA was resuspended

in RNase free water and quantification was performed by spectrophotometry. Denaturing

agarose gel electrophoresis was used to assess the integrity and relative contamination of

mRNA with ribosomal RNA. Extracted mRNA was stored at -80°C until use.

2.3.3 Synthesis of labeled cDNA

Labeled cDNA were prepared using 2 µg of mRNA. Three synthetic poly(A)+tailed

RNA standards were spiked at three different amounts (10 ng, 1 ng and 0.1 ng per reaction)

into the purified mRNA as required by the microarray kit (EAT, Namur, Belgium). The RNA

standards are used for quantification and estimation of experimental variation introduced

during labeling and analysis. For more details concerning the cDNA preparation, please refer

to de Longueville et al. (2002).

2.3.4 Microarray design and hybridization

The DualChip rat hepato (EAT, Namur, Belgium) contains two arrays per slide with a

range of genes involved in basic cellular processes such as drug metabolism, stress responses,

cell proliferation, cell cycle activation, transcription, inflammation, apoptosis (de Longueville

et al., 2002). To evaluate the reliability of the experimental data, several positive and negative

hybridization and detection controls are included on the microarray. For normalization, three

internal standard controls and 8 housekeeping genes were arrayed on the slides.

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The DualChip rat hepato hybridization was carried out according to the

manufacturer’s instructions as reported in de Longueville et al. (2002). The detection was

performed by using a cyanin-3 streptavidin conjugate (Amersham Pharmacia Biotech,

Buckinghamshire, England).

2.3.5 Imaging, statistical analysis and clustering

After Hybridization, arrays were scanned using the GMS 418 laser confocal scanner

(Genetic Microsystem, Woburn, MA, USA) at a resolution of 10 µm. To maximize the

dynamic range of microarrays, the same arrays were scanned using different photomultiplier

settings (PMT). The use of different intensities allows the quantification of both the high and

low copy expressed genes. After image acquisition, the scanned 16-bit image was used to

quantify the signal intensities with the ImaGene 4.1 software (BioDiscovery, Los Angeles,

CA, USA). The fluorescent intensity of each DNA spot (average of intensity of each pixel

present within the spot) was calculated using local mean background subtraction. A signal

was accepted if the average intensity after background subtraction was at least 2.5 fold higher

than its local background. The two intensity values of the duplicate DNA spots were averaged

and used to determine the intensity ratio between the reference and the test samples. Very

bright element intensities (saturated signals, highly expressed genes) were deemed unsuitable

for accurate quantification because they underestimated the intensity ratios and were excluded

from further analysis.

Several potential sources of experimental variation could occur during cDNA synthesis,

labeling, hybridization and indirect detection steps. To take into account these possible

variations, the data were normalized in a two step procedure. The values were first corrected

using a factor calculated from the intensity ratios of the internal standards in the references

and test samples. The presence of the three internal standard probes at two different locations

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of the microarray allowed to measure a local background and to evaluate the microarray

homogeneity, which is taken into account in the normalization (Schuchhardt et al., 2001).

However, as the internal standard control does not take into account the purity and quality of

the mRNA, a second step of normalization was performed based on the expression levels of

the housekeeping genes. This process involved calculating the average intensity from a set of

housekeeping genes. Among these housekeeping genes, only genes for which the expression

was not changed after a particular treatment were taken into account for the normalization.

Indeed, any drug may affect the expression of some of the housekeeping genes.

The variance of the normalized set of housekeeping genes (except those affected by

the treatment) was used to generate an estimate of expected variance, leading to a predicted

confidence interval (CI) to test the significance of the ratios obtained (Chen et al., 1997; de

Longueville et al., 2002). Ratios outside the 99% confidence interval were determined to be

significantly different. The analysis of variance (ANOVA) was used to examine the data.

Before performing the cluster analysis, ratios falling inside the 99 % confidence

interval were replaced by the value 1. Clusters of hybridization profiles were created with the

s-plus 2000 software (Insightful, Seattle, WA, USA) using the classical agglomerative

hierarchical with the single link. The distance computed between two hybridization profiles

corresponds to the Manhattan distance (Van Custem et al., 1994).

2.4. Validation of relative gene expression by real-time PCR

The single strand-cDNA (ss-cDNA) was synthesized from 0.5µg mRNA according to

the RNA labeling protocol described in de Longueville et al. (2002) with the following minor

modifications: (1) a DNAse treatment of mRNA was performed prior to cDNA synthesis; (2)

the dNTP mixture contained dGTP, dATP, dTTP and dCTP each at 500µM but no

biotinylated dCTP; (3) the second addition of reverse transcriptase was omitted.

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Gene specific primers correspond to the gene sequence present on the DualChip rat

hepato (EAT, Namur, Belgium). Forward and reverse primers for real-time PCR amplification

were designed with the Primer Express Software (PE Applied Biosystem, Foster City, CA,

USA).

Real time PCR was performed on 6 genes, namely, CYP 2B1/2, CYP 3A, GST Ya,

Smp30, GAPDH (house keeping gene) and ribosomal protein S9 (house keeping gene).

mRNA extracted from hepatocytes exposed to PB and CLO was used in the real time PCR

(n=2) and each reaction was performed in triplicate.

PCR reaction mixtures contained of 12.5 µl SYBR green PCR Master Mix 2X (PE

Applied Biosystems, Foster City, CA, USA), 2.5µl forward primer (3mM), (PE Applied

Biosystems, Foster City, CA, USA), 2.5µl reverse primer (3mM) (PE Applied Biosystems,

Foster City, CA, USA), 5µl cDNA and 2.5 µl distilled water. PCR reactions without cDNA

were performed as template-free negative controls. All PCR reactions were made in

duplicates with the following PCR conditions: 2 min at 50 °C, 10 min at 95 °C followed by 40

cycles of 15 s at 95 °C and 1 min at 60 °C in 96-well optical plates (PE Applied Biosystem,

Foster City, CA, USA) in the ABI 7000 Sequence Detection System (Perkin-Elmer life

Sciences, Boston, MA, USA). The ABI PRISM 7700 sequence detection system software

(version 1.6) was used for data analysis according to the manufacturer’s instructions (PE

Applied Biosystem, Foster City, CA, USA).

Fluorescence emission was detected for each PCR cycle and the threshold cycle (CT)

values were determined. The CT value was defined as the actual PCR cycle when the

fluorescence signal increased above the background threshold. Average CT values from

duplicate PCR reactions were normalized to average CT values for housekeeping gene from

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the same cDNA preparations. The ratio of expression of each gene in hepatotoxicants treated

vs. vehicle sample was calculated as 2-(∆∆CT) of that treatment as recommended by Perkin-

Emer where CT is the threshold cycle and ∆∆CT is the difference CT (test gene) - CT

(housekeeping gene) for treated sample minus vehicle sample. Values were reported as an

average of triplicate analyses.

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3. Results

3.1. Cytotoxicity assessment

Incubation for 24h with AM, ANIT, ERY, tetracycline, CLO, 4-pentenoic acid and PB

decreased the MTT reduction in freshly isolated male rat hepatocytes in a concentration

dependent manner, with EC50 values of 14, 17, 64, 787, 2511, 6288 and 11253 µM,

respectively (Table 1 and Fig. 1). The BNF EC50 value of 30 µM was estimated by graph

extrapolation. EC50 values could not be calculated for zileuton, ISN, APAP in male

hepatocytes because only mild effects were observed even for the highest tested

concentrations. For these three compounds, the maximal reduction of the MTT end-point was

35, 12, and 18% respectively for 300 µM zileuton, 10 mM ISN and APAP (male).

Consequently, EC50 values for zileuton, ISN and APAP (male) are higher than the highest

concentrations tested. Finally, the female rat hepatocytes were more susceptible to APAP

toxicity, with a decrease in the MTT metabolism of 67% compared to only 18% in male rat

hepatocytes at a concentration of 10 mM.

3.2 Analysis of gene expression modifications induced by the hepatotoxicants

3.2.1. Vehicle treated samples

Hepatocellular gene expression changes induced by DMSO and DMF (the solvents used

to dissolve the test compounds) are shown in Fig. 2. Acyl-Co-oxidase, involved in peroxisome

proliferation and HGPT (hypoxanthine guanine phosphoribosyl transferase), a housekeeping gene,

were respectively up- and down-regulated, by DMSO (p<0.01). On the other hand, two genes

implicated in stress responses, namely GSH reductase and MDR-1b (multi-drug resistance) were

down-regulated by DMF (p<0.01).

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3.2.2 Hepatotoxicant treated samples

A global view of the different gene expression profiles induced by the various treatments

is presented in Fig. 3 as well as an example of the microarray pictures obtained after

hybridization.

Cytochrome P450s inducers

Different subfamilies of CYP P450 1A, 2B, 3A and 4A were significantly induced by

BNF, PB, zileuton and CLO (p<0.01, Fig. 3 and Fig 4A). Expression of the major CYP P450

genes was increased by a factor of at least 25. For example BNF, PB and CLO induced CYP1A1,

2B, and 4A1 by a factor 25.41, 45.01 and 33.03 respectively. In addition, PB decreased the

expression of CYP4A1 (Fig. 3 and 4A). An overall view of the data is presented in Fig. 4B. CLO

treatment significantly changed the expression of 12 genes included on the microarray versus 7

genes for zileuton and 6 genes for PB and BNF.

Drugs inducing hepatocellular necrosis

The data revealed significant gene expression changes for 10 and 11 genes after a

treatment with ISN and APAP, respectively (Fig. 3 and 4B). A comparison among the drugs

inducing necrosis shows that 4 genes implicated in phase I metabolism (CYP3A1 and Acyl-

coA-oxidase), phase II metabolism (Glutathione S-transferase Ya) (GST Ya) and growth arrest

and DNA damage response (GADD153) followed the same tendency (Fig. 4). In addition, the

gene expression profiles were not identical between female and male hepatocytes exposed to

APAP. Seven genes were differentially expressed in response to APAP. APOJ, albumin,

fibronectin were induced to a greater extent in female hepatocytes whereas MDR-1b and

Ornithine decarboxylase (ODC) were only repressed in male hepatocytes treated with APAP,

respectively. In addition, the UDP-glucuronosyltransferase gene (UDPGT 1A6) implicated in

phase II metabolism was differentially regulated in male and female hepatocytes exposed to

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APAP. Finally, 7 genes followed the same tendency after treatment to APAP in male and

female hepatocytes (Fig. 4B).

Drugs inducing hepatocellular cholestasis

The expression of 3 genes involved in apoptosis (Bcl-2), phases I (CYP 3A1) and II

(UDPGT 1A) metabolism were significantly up-regulated in rat hepatocytes after a treatment with

ERY whereas Bax was down-regulated (Fig. 3 and 4C). On the other hand, ANIT induced the

expression of 12 genes involved in phases I (CYP3A1) and II (GST Ya and theta5) metabolism,

apoptosis (Bax, Bcl-2), oncogenesis (c-Myc), stress response (Hsp 70, MnSOD; manganese

superoxide dismutase), cell proliferation (PCNA; proliferation cellular nuclear antigen), cellular

markers such as alpha-2-macroglobulin, structural element like fibronectin and arginine synthesis

(ODC). The gene expression comparison between both treatments (ERY and ANIT) revealed that

only 2 genes had the same gene expression profile (CYP 3A1 and Bcl-2) whereas the expression

of Bax was either down- or up-regulated in hepatocytes exposed to ERY or ANIT.

Tetracycline, pentenoic acid and amiodarone

After tetracycline treatment, the expression of only 3 genes was changed; CYP4A1

and GADD153 were up-regulated while senescence marker protein-30 (Smp30) was down-

regulated (Fig. 3 and 4D). Three genes were also found to be differentially expressed after

pentenoic acid and AM treatments. CYP4A1 and C-Jun were up-regulated and Smp30 was

down-regulated by pentenoic acid. On the other hand, AM significantly induced the

expression of cytochrome-c-oxidase and CYP4A1 and repressed the expression of Smp30.

Both CYP 4A1 and Smp30 transcript levels were significantly up- and down-regulated by

tetracycline, pentenoic acid and AM (Fig. 3 and 4D).

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3.3. Gene expression validation

Based on data collected from the microarray technique and presented in Fig 4A, the

expression profile was validated for 6 genes responsive to CLO and PB treatment (CYP

2B1/2, CYP 3A, GST Ya, Smp-30, GAPDH and ribosomal protein S29) (Fig. 5).

Real time PCR data revealed that CYP 2B1/2, CYP 3A1, and GST-Ya were induced

189.2, 14.66 and 3.95 times in PB treated hepatocytes (Fig. 5B). On the other hand, the

expression of smp30 was repressed by a factor 0.56 in the cells exposed to PB. The

expression of GADPH and ribosomal S29 was not affected by PB as the ratios were quite

close to 1. Fig. 5B shows as well that CLO changed the expression of CYP 2B1/2, CYP 3A1

and GST-Ya by a factor 100.33, 1.97, and 2.07. The expression of smp30 is not affected by

CLO as the ratios were quite close to 1.

The gene expression measured by the means of DNA microarrays followed the same

tendency for the 6 genes measured

3.4. Clustering analysis

The comparison of all gene expression profiles generated by the 11 reference

compounds revealed that six different clusters were observed (Fig. 6). The first cluster

contains tetracycline, pentanoic acid and AM. The second one includes APAP, ANIT, ERY,

and ISN. Four other clusters were formed by individual drugs that are BNF, PB, zileuton and

CLO.

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4. Discussion

In the present study, we used a low-density microarray containing 59 genes to analyze

the gene expression profiles generated in primary cultures of rat hepatocytes exposed to 11

different known hepatotoxicants. The drugs were pooled into 4 groups labeled necrosis (ISN

and APAP), cholestasis (ERY and ANIT), steatosis (tetracycline, 4-pentenoic acid and AM),

and induction of CYP P450 subfamilies (CLO, BNF, PB and zileuton). Our main goal was to

analyze changes in gene expression levels induced by these drugs and to determine if the

transcription profiles would permit a classification of compounds associated signatures.

Male hepatocytes were used for all drugs except for APAP for which female

hepatocytes were also used. Indeed, APAP is known to produce sex-dependent hepatotoxicity

in young adult rats (Tarloff et al., 1996) and consequently we expected to see some

modifications in gene expression between male and female hepatocytes after a treatment with

APAP. Incubation conditions with compounds can vary considerably between toxicological

studies leading to various results.

Based on a previous study dealing with the kinetic of gene expression (hepatocytes

exposed to PB for 24, 48 or 72 h), 24h was selected because this time point could provided for

PB a good gene expression response. 24 h was also selected for the other drugs used in this

study because it would most likely provide a complete gene response in hepatocytes without

the interference of significant secondary responses that could be encountered at later. In

addition, in vitro, a time of 24 h after treatment has been frequently selected to analyze gene

expression modifications (Baker T.K et al.; 2001, Waring et al., 2001a). The range of

concentration tested in the cytotoxicity assay was based on preliminary assays using a wide

range of concentrations and concentrations of the different compounds selected for the gene

expression experiment were chosen based on cell viability assays (MTT curve). For all

compounds, the selected concentrations did not induce any cell death (even for female

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hepatocytes exposed to APAP after curve correction see Fig. 1) but were generally close to

the lowest concentrations inducing cell mortality. This approach was used since literature data

were not available for many compounds regarding to the concentrations required to elicit

toxic effects on hepatocytes either under in vitro nor in vivo conditions. Based on

mechanisms of action and differential induced-toxicity, it is almost impossible to test the

different classes of molecules in the same range of concentrations. For example, toxicity is

observed at different doses for the different molecules.

Although the mode of action of BNF, CLO, PB, and zileuton are differents, we

arbitrarily pooled these drugs because they are inducers of cytochrome P450 isoforms

(Sundseth and Waxman, 1992; Gerhold et al., 2001). BNF is an aromatic hydrocarbon that

induces the expression of CYP1A family by activating the Aryl hydrocarbon (Ah) receptor

(Denison and Heath-Pagliuso, 1998). In the present study, induction of gene encoding

CYP1A and several phase II enzymes namely UDPGT1A6 and GST Ya was also observed in

rat hepatocytes after a BNF treatment as reported elsewhere (Maheo et al., 1997; Saarikoski et

al., 1998).

The barbiturate PB induces the transcription of the rat gene CYP2B and CYP3A

(Frueh et al., 1997; Meyer and Hoffmann, 1999) through the constitutive androstane receptor

(CAR) (Honkakoski et al., 1998; Masahiko and Honkakoski, 2000). In our study, PB induced

CYP2B, CYP3A and repressed CYP4A1. CYP4A1 is involved in the ω-hydroxylation of

fatty acids (Gibson et al., 1982) and therefore a down regulation could lead to cellular

dysfunction. Induction of genes encoding phase II metabolism enzymes (GST Ya and

UDGT1A) was also observed in our study. In addition, the mRNA level of the senescence

marker protein-30 (Smp30) was significantly reduced following PB treatment as reported in

other studies (Fujita et al., 1999).

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CLO, a lipid lowering agent, triggers peroxisome proliferation in rodents and induces

genes involved in the β-oxidation of fatty acids by the activation of the peroxisome

proliferator-activated receptor alpha (PPARα) (Simpson, 1997; Lindquist et al., 1998; Corton

et al., 2000). CLO is known to induce members of the CYP4A subfamily genes (Surry et al.,

2000 and present study). CLO significantly induced the expression of CYP 2B, CYP 3A, GST

ya, theta5 and UDPGT1A as reported elsewhere (Ritter and Franklin, 1987; Ronis et al.,

1994; Jemnitz et al., 2000). In our study, increased lipid β-oxidation in response to CLO is

supported by the induction of genes encoding peroxisomal enzymes such as acyl-CoA oxidase

and peroxisomal enoyl-CoA-hydratase.

Zileuton, a 5-lipoxygenase inhibitor, is considered as a moderate inducer of CYP 450

(Rodrigues and Machinist, 1996) and it induced CYP2B in our study. However, zileuton also

significantly modified the expression of GADD153, Erk1, Histone deacetylase (Hdac),

Ferritin subunit H, fibronectin and HMG-CoA-synthetase (3-hydroxy-3methylglutaryl-CoA-

synthetase). A comparison of our data with other studies is difficult since, to our knowledge,

the effect of zileuton on gene expression has not been published yet.

The next compounds studied were ISN and APAP which are known to induce

necrosis. ISN is a first-line drug in the prophylaxis and treatment of tuberculosis (Sadaphal et

al., 2001). Little is known about the effect of ISN on gene expression. In our study, ISN

induced CYP3A, GST Ya, GADD153, Hsp70, HO-2 (heme oxygenase 2), transferin and

cytochrome c-oxidase.

APAP is known to induce the depletion of glutathione and cell death (Ray and Jena,

2000), impair the mitochondrial respiration (Burcham and Harman, 1991) and interfere with

Ca++ homeostasis (Salas and Corcoran, 1997). However so far, the exact mechanism of action

of acetominophen has not been completely elucidated although recent reports have identified

the constitutive androstane receptor as a regulator of APAP hepatotoxicity (Zhang and Huang,

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2002). APAP is mainly metabolized by cytochromes P450 (CYP 3A), by glucuronidation and

sulforination pathways (Tygstrup et al., 2002). Our data show that, APAP changed the

expression of genes implicated in drug metabolism (induction of CYP 3A1, GST Ya,

UDGT1a, UDGT1a6), stress response (repression of MDR-1b, induction of Hsp70), DNA

repair (induction of GADD153 and repression of MGMT) and oncogenesis (induction of c-

myc). It is noteworthy that GADD 153 and Hsp70 have been associated with the induction of

apoptosis (Fontanier-Razzaq et al., 1999; Reilly et al., 2001). APAP is also known to produce

sex-dependent hepatotoxicity in young adult rats (Tarloff et al., 1996). Our data reveal some

important gene expression differences between male and female rat hepatocytes. For instance,

APOJ, albumin and fibronectin were only induced in female hepatocytes, whereas repression

of MDR-1b and induction of ODC only occurred in male hepatocytes.

Intra-hepatic cholestasis has been reported to occur during ERY and ANIT therapy

(Orsler et al., 1999). In our study, CYP3A and Bcl-2 were induced by both drugs as reported

elsewhere (Que et al., 1997; Celli et al., 1998) and Bax was repressed and induced by ERY

and ANIT, respectively. Aoshiba and co-workers (1995) also reported the effects of ERY on

apoptotic genes. The increased of Bcl-2 expression is protective against apoptosis due to its

intracellular antioxidant action (Gottlieb et al., 2000). In addition to these apoptotic genes,

ANIT up-regulated GST-Ya and theta 5 as already reported by other studies (Lesage et al.,

2001; Ohta et al., 2001), PCNA (Ranganna et al., 2000), c-myc, Hsp70, fibronectin, alpha-2-

macroglobulin and ODC. However, the correlation between ANIT toxicity and these genes is

not yet established.

Tetracycline, pentenoic acid and AM are known to induce hepatocellular steatosis

(Loscher et al., 1993; Fromenty and Pessayre, 1995). In the present study, these three

compounds up- and down-regulated CYP4A1 and Smp30, respectively. Smp30 seems to play

a critical role in the highly differentiated functions of the liver and its down-regulation may

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contribute to hepatic deterioration of cellular functions induced by steatosis (Fujita and

Shirasawa, 1999; Ishigami et al., 2002). CYP4A induction always accompanies any

substantial drug-dependent increases in beta-oxidation (Tang et al., 1995; Amacher and

Martin, 1997). Robertson and co workers (2001) suggested that the induction of CYP4A

could be used as a good marker to assess steatosis injury. Other genes were differently

expressed after tetracycline, pentenoic acid and AM treatments. For instance, tetracycline

induced the expression of GADD153, a growth arrest and DNA damage gene. On the other

hand, pentenoic acid induced C-Jun, a nuclear transcription factor and such events may lead

to toxic events (Kovary and Bravo, 1991; Chung et al., 2001).

Microarray measurements are usually semi-quantitative, with compression of values

occurring at high-fold changed (Gerhold et al., 2001; Rajeevan et al., 2001; Yuen et al., 2002)

but generally the data generated by microarray are in agreement with real time PCR results. In

the present study, it was shown that the 6 genes measured with both technologies followed the

same tendency for PB and CLO treated hepatocytes. Indeed, a compression of the values

occurs at high-fold changes in expression but as observed, the quantifications made by the 2

methods are well correlated.

Even with a relatively limited gene set, all the 11 compounds gave rise to discernable

gene expression profiles as already obtained with high-density microarrays (Hamadeh et al.,

2002b; Morgan et al., 2002). When clustering analysis is performed, it has to be noted that

drugs inducing similar endpoints (e.g. cholestasis) may trigger different mechanism of

actions. Thus, such drugs will not necessarily change the expression of the same set of genes.

BNF, PB, CLO and zileuton arbitrarily pooled in the CYP450 inducers formed four individual

clusters, which confirmed that they act through different mechanisms of action. The present

study also shows that some compounds belonging to the same class of toxicant were linked,

suggesting that they target similar genes and possibly through the same mechanism of action.

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For instance, a cluster was formed for tetracycline, pentenoic acid and amiodarone, drugs that

are known to induce steatosis. On the other hand, the drugs inducing necrosis (APAP and

ISN) and cholestasis (ANIT and ERY) were pooled in the same cluster which can be divided

in two sub-clusters (firstly: ANIT and APAP and secondly: ISN and ERY). Interestingly,

APAP and ANIT which belong to the cholestasis and necrosis groups, respectively, are also

known to be inducers of apoptosis. Thus, this may explain why both drugs were clustered

together. However, it has to be said that the low number of genes studied may also diminish

the power of the clustering analysis.

The use of cultured hepatocytes to model hepatotoxicity has proven to be a valuable

tool despite some limitations (see introduction for more details). Some reports have shown

that gene expression data showed a good correlation between in vitro and in vivo models. For

instance, hepatocytes treated with PPARα agonist fenofibrate produced gene expression

changes characteristic of the in vivo response in rat liver (Baker et al., 2003). The data

revealed remarkable similarities in both the affected biological pathways and the rank-order

magnitude of the response. The present study shows also a good correlation with regard to

induction and repression of gene expression obtained in primary rat hepatocytes when

compared to in vivo data.

Low-density microarrays seem to represent a useful tool to select drug candidates

early in the development in conjunction with other data (e.g. toxicokinetic and

pharmacological studies). For instance, drugs that do not change the expression of genes

implicated in phase-1 and -2 metabolisms could be of particular interest. In addition, another

attractive application could be to compare gene expression patterns generated by a key

compound and its analogs. This would allow the selection of the best analogs based on gene

expression comparison.

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In conclusion, the in vitro gene expression data generated in this study were in good

agreement with the literature which mainly concerns in vivo data. Furthermore, gene

expression profiles observed in this study have been confirmed for several genes by Real-time

PCR assays. This confirmation validates our results and supports the use of microarray

technology in toxicogenomic. Each drug gave unique gene expression profile. Despite the low

number of genes studied, the gene expression patterns allowed a certain degree of

classification of compounds with similar hepatocellular injuries. Finally, low-density

microarrays represent a powerful tool to investigate mechanistic toxicology issues and to help

in the selection of the best drug candidates in conjunction with other data.

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Acknowledgments

This work was supported by the Region Wallonne, Belgium. Furthermore, we would like to

thank Anne-France Dabee for her technical assistance.

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Figure Legends

FIG 1. MTT reduction in rat hepatocytes treated for 24 h with various hepatotoxicants. The

identities of the drugs are indicated in each figure. Male hepatocytes were used with all

toxicants, but for APAP, female hepatocytes (F) were also used. Rat hepatocytes were

incubated for 24 h with the different hepatotoxicants before assessing the cell viability by

MTT reduction. Results (means ± standard deviation for triplicate wells) were expressed as a

percentage of the MTT determination for control cells incubated with control solvent. The

arrows indicate the concentration of toxicant used in the gene expression experiment. For

BNF, the concentration used was 2 µM. For the exact toxicant concentrations, please refer to

Table 1. * and ** means precipitation of the compound in the test medium and acidification

of the test medium, respectively.

FIG 2. Logarithmic scatter plots of normalized fluorescence intensity values from DualChip

rat hepato hybridized with cDNA obtained from mRNA extracted from DMSO-treated

hepatocytes (A) and from DMF-treated hepatocytes (B) versus reference (hepatocytes in WDI

medium). The arrows indicate the genes that are significantly up- or down-regulated by the

solvent treatment (p<0.01).

FIG. 3. Gene expression profiles of DualChip rat hepato hybridized with cDNA obtained

from mRNA extracted from control and compound treated rat hepatocytes (n=3). A) The data

are expressed as mean ratio (treatment vs. reference as well as solvent vs. WDI) outside the

99 % confidence interval. The range of changes is represented by a code of colors at the

bottom of the chart. House keeping genes appear in red. Individual genes are grouped into

functional classes. Genbank accession numbers are also given under the column heading ‘Acc

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#’. All compounds were dissolved in DMSO except CLO in DMF. B) Dualchip rat hepato

hybridized with cDNA obtained from mRNA extracted from reference and BNF treated

sample. Fluorescence is represented in pseudocolor scale and corresponds to the expression

levels of genes. The arrows show two examples of changes in gene expression levels

(induction of CYP 1A1 and GST Ya in BNF treated hepatocytes). For the description of each

spot, please refer de Longueville et al. (2002).

FIG. 4. Gene expression profiles obtained from rat hepatocytes exposed to A) BNF, PB, CLO

or zileuton, B) ISN and APAP, C) ERY and ANIT and D) tetracycline, pentenoic acid and

AM. Expression data are presented as a mean ratio (treatment vs. reference) (n=3, p<0.01).

Only genes having a significant ratio are presented in the table. The red and green codes

correspond to up and down regulated genes, respectively. The gray code means no significant

change between treatment and reference (p<0.01). Genbank accession numbers are also given

under the column heading ‘Acc #’.

FIG. 5. Comparison of gene expression data determined by DNA microarray and real-time

PCR. A) Real-time PCR SYBR Green I Fluorescence versus cycle number of CYP 2B1/2

gene and reference gene (ribosomal protein S29) in PB treated sample and vehicle treated

sample. The data for ribosomal protein S29 in the two samples, for CYP2B1/2 in PB treated

sample and for CYP2B1/2 in vehicle treated sample is indicated as A, B, and C respectively,

in the amplification plot. Closed arrowhead shows PCR with no template. Striped arrow

indicates the position of the noise band. B) Comparison of microarray and real time PCR gene

expression data. Microarray data derived from Fig. 4A are shown as mean ratios (treatment

vs. reference, n=3, p<0.01). The red and green codes correspond to up and down regulated

genes, respectively. The gray code means no significant change between treatment and

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reference (p<0.01). House keeping genes appear in red. Genbank accession numbers are also

given under the column heading ‘Acc #’.

FIG. 6 Clustering analysis of the gene expression patterns induced by the 11 hepatotoxicants.

A cluster was formed for tetracycline, pentenoic acid and AM (red color). The drugs APAP,

ISN, ANIT and ERY were pooled in the same cluster which can be divided in two sub-

clusters (firstly: ANIT and APAP and secondly: ISN and ERY) (blue color). BNF, PB,

zileuton and CLO formed four individual clusters (in pink, brown, green and black color,

respectively). The drugs inducing cholestasis, necrosis and steatosis are represented by *, •,

and ♦, respectively.

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TABLE 1. Range of compound concentrations and EC50 values determined in the MTT

assay as well as chemical concentrations used in the experiment for gene expression

analysis. Drugs are classified from the lowest to the highest concentration used in the gene

expression experiment. (a) EC50 not calculated, graphically extrapolated. (b) EC50 determined

with inclusion of the MTT data measured in acidified medium. (c) For BNF, a concentration

outside the range tested in the cytotoxicity experiment was used because the lowest

concentration (10 µM) induced around 10 % mortality (Fig. 1).

MTT reduction assay Gene Expression

experiment

Compounds Range of tested

concentrations (µM)

EC50 (µM) Concentration

used (µM)

Amiodarone (AM) 1-100 14 1.5

β-naphtoflavone (BNF) 10-1000 30(a) 2(c)

α-naphtylylisothiocyanate (ANIT) 0.3-100 17 3

Erythromycin estolate (ERY) 1-100 64 6

Zileuton 10-300 >300 30

Tetracycline 10-3000 787 120

4-pentenoic acid 100-10000 6288(b) 200

Clofibrate (CLO) 10-3000 2511(b) 400

Acetaminophen (F, APAP) 100-10000 8110 1000

Acetaminophen (APAP) 100-10000 >10000 1000

Isoniazid (ISN) 100-10000 >10000 1000

Phenobarbital (PB) 100-15000 11253 1000

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* *0

20

40

60

80

100

120

140

-6 -5.5 -5 -4.5 -4

Amiodarone concentration (logM )

MTT

redu

ctio

n (%

con

trol

cel

ls)

*0

20

40

60

80

100

120

140

-6.5 -6.0 -5.5 -5.0 -4.5 -4.0

ANIT concentration (logM)

MTT

redu

ctio

n (%

con

trol

cel

ls)

***

0

20

40

60

80

100

120

140

-5 -4.5 -4 -3.5 -3

β -naphthoflav one conce ntration (logM )

MTT

redu

ctio

n (%

con

trol

cel

ls)

0

20

40

60

80

100

120

140

-6 -5.5 -5 -4.5 -4Erythromycin concentration (logM )

MTT

redu

ctio

n (%

con

trol

cel

ls)

*0

20

40

60

80

100

120

140

-5 -4.5 -4 -3.5 -3 -2.5Te tracycline conce ntration (logM )

MTT

redu

ctio

n (%

con

trol

cel

ls)

**

0

20

40

60

80

100

120

140

-5 -4.5 -4 -3.5 -3 -2.5Clofibric acid conce ntration (logM )

MTT

redu

ctio

n (%

con

trol

cel

ls)

0

20

40

60

80

100

120

140

-4 -3.5 -3 -2.5 -2

Ace taminophe n conce ntration (logM )

MTT

redu

ctio

n (%

con

trol

cel

ls)

0

20

40

60

80

100

120

140

-4 -3.5 -3 -2.5 -2Acetaminophen concentration (logM)

MTT

redu

ctio

n (%

con

trol

cel

ls) (F)

0

20

40

60

80

100

120

140

-4 -3.5 -3 -2.5 -2Isoniazid conce ntration (logM )

MTT

redu

ctio

n (%

con

trol

cel

ls)

****

**

0

20

40

60

80

100

120

140

-4 -3.5 -3 -2.5 -2

4-pe nte noic acid conce ntration (logM )

MTT

redu

ctio

n (%

con

trol

cel

ls)

0

20

40

60

80

100

120

140

-4 -3.5 -3 -2.5 -2 -1.5Phe nobarbital conce ntration (logM )

MTT

redu

ctio

n (%

con

trol

cel

ls)

0

20

40

60

80

100

120

140

-5 -4.5 -4 -3.5Zile uton conce ntration (logM )

MTT

redu

ctio

n (%

con

trol

cel

ls)

40

FIG 1.



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A)

10

100

1000

10000

100000

10 100 1000 10000 100000

Reference

DM

SO-tr

eate

d ra

t hep

atoc

ytes

GenesHKGy=xp<0.01p<0.01

ACO

B)

10

100

1000

10000

100000

10 100 1000 10000 100000

Reference

DM

F-tr

eate

d ra

t hep

atoc

ytes

Genes

HKG

y=x

p<0.01

p<0.01

41

HGPT

GSH reductase

MDR 1b

FIG 2.



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> -10

> -5

> -2

> -1.5

No Change > 1.5

>2 >5 > 10

Gene Description

Acc #

BN

F

PHE

NO

BA

RB

ITA

L

CL

OFI

BR

AT

E

ZIL

EU

TO

N

ISO

NIA

ZID

APA

P (m

ale)

APA

P (f

emal

e)

ER

YT

HR

OM

YN

CIN

AN

IT

TE

TR

AC

YC

LIN

PEN

TA

NO

IC A

cid

AM

IOD

AR

ON

E

DM

F

DM

SO

CYP P450 Inducers Necrosis Cholestasis Steatosis Control Apoptose

Bax/ apotosis inducer U49729 Bcl-2 L14680 Tumor necrosis factor (TNF) X66539

GSH metabolism Glutathione-S-transferase Ya K01931 Glutathione-S-transferase theta 5 X67654

Cytochrome P450 metabolism CYP1A1 X00469 CYP1B1 U09540 CYP2B1/2 M34452 CYP3A1 M10161 CYP4A1 X07259

Peroxisome Proliferators Acyl-CoA Oxidase J02752 PPAR-α M88592 Peroxisomal Enoyl-CoA hydratase K03249 Phospholipase A2 D00036

Glucuronyl Transferase UDPGT1A J05132 UDPGT1A6 D83796

Transcription C/EBP-α X12752 Iκβ-α U66479 NFκβ L26267 P38 U73142

Oncogene c-Myc Y00396 Elk X87257

Stress/Cell Damage Responses ApoJ M16975 Cytochrome c oxidase M27315 GSH reductase U73174 Heme oxygenase 2 J05405 Hsp70 L16764 MDR-1b M81855 MnSOD Y00497 TGF-b type II L09653 O-6-methylguanine DNA methyl transferase

M76704

GADD45 L32591 GADD153 U30186

Inflammation Cox-2 L20085 Il6 M26744

Cell Proliferation c-Jun oncogene/transcription factor AP1 X17163 Extracellullar signal regulated kinase 1 (erk1)

M61177

Nuclear oncoprotein p53 X13058 Proliferating cell nuclear antigen (PCNA) Y00047 Smp30 X69021 Histone D-acetylase (hdac1) NM_008228

Cell Cycle Activation Cyclin D1 D14014 Jnk-1 L27129 Telomerase protein component 1 U89282

A)

FIG 3.

42



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Gene Description

Acc #

BN

F

PHE

NO

BA

RB

ITA

L

CL

OFI

BR

AT

E

ZIL

EU

TO

N

ISO

NIA

ZID

APA

P (m

ale)

APA

P (f

emal

e)

ER

YT

HR

OM

YN

CIN

AN

IT

TE

TR

AC

YC

LIN

PEN

TA

NO

IC A

cid

AA

MIO

DA

RO

NE

DM

F

DM

SO

CYP P450 Inducers Necrosis cholestasis Steatosis Control Transporters

Serum Albumin VO1222 Ferritin Subunit H U58829 Transferin D38380

Growth factor Hepatocyte growth factor D90102

Cellular markers α-2-Macroglobulin J02635 Poly ubiquitin D16554 Structural Elements/Cell Mobility β-actin V01217 Fibronectin X15096 Tubulin-α V01227 Myosin heavy chain 1 (myr) X68199

Argine synthesis (Urea cycle) Ornithine decarboxylase J04791

Cholesterol metabolism HMG Co A Synthetase X52625

Carbohydrate Metabolism GAPDH X02231

Protein Synthesis Ribosomal protein S29 X59051

Purine salvage pathway HGPT M86443

> -10

> -5

> -2

> -1.5

No Change > 1.5

> 2

> 10

B)

Reference BNF

Max Min

Fluorescent intensity level

FIG 3.

43



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A) CYP P450 Inducers

Gene Description Acc # BNF PB CLO ZILEUTON

Phase I metabolism CYP1A1 X00469 25.41 CYP2B1/2 M34452 45.01 6.43 30.53 CYP3A1 M10161 5.06 2.01 CYP4A1 X07259 0.52 33.03

Phase II metabolism Glutathione-S-transferase Ya K01931 1.84 5.59 2.35 Glutathione-S-transferase theta 5 X67654 1.76 UDPGT1A J05132 1.72 1.55 UDPGT1A6 D83796 2.03

Phase III Acyl-CoA Oxidase J02752 8.60 Peroxisomal Enoyl-CoA hydratase K03249 2.78 GADD153 U30186 0.45 Extracellullar signal regulated kinase 1 (erk1) M61177 0.55 Smp30 X69021 0.37 Histone D-acetylase (hdac1) NM_008228 0.48 Cyclin D1 D14014 0.46 Ferritin Subunit H U58829 0.43 α-2-Macroglobulin J02635 0.48 Fibronectin X15096 0.51 0.51 0.5 HMG Co A Synthetase X52625 2.02 0.37 Bax/ apotosis inducer U49729 0.53 Transferin D38380 1,66

B) Necrosis Inducers

44

Gene Description Acc # ISN APAP (male) APAP (female)

Phase I metabolism CYP3A1 M10161 4.65 3.02 10.01

Phase II metabolism UDPGT1A J05132 0.68 0.67 UDPGT1A6 D83796 0.48 2.80 Glutathione-S-transferase Ya K01931 2.40 1.87 2.00 Acyl-coA Oxidase J02752 3.17 2.22 1.86

Oncogene c-Myc Y00396 1.94 3.70

Stress/Cell Damage Responses ApoJ M16975 2.77 Cytochrome c oxidase M27315 2.33 Heme oxygenase 2 J05405 2.70 Hsp70 L16764 3.11 MDR-1b M81855 0.44 O-6-methylguanine DNA methyl transferase M76704 0.49 0.53 GADD153 U30186 3.20 2.40 2.69

Transporters Serum Albumin VO1222 2.08 Transferin D38380 2.00 Fibronectin X15096 2.21

Argine synthesis Ornithine decarboxylase J04791 2.04

Carbohydrate Metabolism GAPDH X02231 2.12

Purine salvage pathway HGPT M86443 2.30 1.77

FIG 4.



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C) Cholestasis Inducers Gene Description Acc # ERY ANIT

Apoptose Bax/ apotosis inducer U49729 0.61 3.80 Bcl-2 L14680 1.86 5.60

Phase I metabolism CYP3A1 M10161 1.94 3.03

Phase II metabolism Glutathione-S-transferase Ya K01931 1.65 Glutathione-S-transferase theta 5 X67654 1.93 UDPGT1A J05132 1.95

Oncogene c-Myc Y00396 2.20

Stress/Cell Damage Responses Hsp70 L16764 2.44 MnSOD Y00497 2.07

Cell proliferation Proliferating cell nuclear antigen (PCNA) Y00047 3,.40

Cellular markers α-2-Macroglobulin J02635 2.50

Structural Elements Fibronectin X15096 2.12

Argine synthesis Ornithine decarboxylase J04791 2.66

D) Steatosis Inducers

45

Gene Description Acc # TETRACYCLIN PENTENOIC ACID AM

Phase I CYP4A1 X07259 2.93 2.34 1.77

Stress/Cell Damage Responses Cytochrome c oxidase M27315 1.79 GADD153 U30186 3.74

Cell Proliferation Smp 30 X69021 0.69 0.54 0.44 c-Jun oncogene/transcription factor AP1 X17163 2.08

FIG 4.



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A)

Cycle Number

Del

ta R

n

Delta Rn vs Cycle

Cycle Number

Del

ta R

n

Delta Rn vs Cycle

A B C

B)

PB CLO

Gene Description Acc # DNA microarray

Real-Time PCR

DNA microarray

Real-Time PCR

Phase I Metabolism CYP2B1/2 M34452 45.01 189.2 6.43 100.33 CYP3A1 M10161 5.06 14.66 2.01 1.94

Phase II Metabolism Glutathione-S-transferase Ya K01931 5.59 3.95 2.35 2.07

Cell Proliferation Smp30 X69021 0.37 0.56 0.98 1.15

Carbohydrate Metabolism GAPDH X02231 0.71 0.78 1.05 1.03

Protein Synthesis Ribosomal protein S29 X59051 1.14 1.27 1.11 0.99

FIG 5.

46



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BNF

ISN•

ERY*

TETRACYCLINE♦

PENTANOIC Acid♦

AM♦

APAP male•

APAP female•

ANIT*

PB

ZILEUTON

CLO

+ Similarity -

FIG 6. 47



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induced by hepatotoxicants on primary cultures of rat hepatocytes

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